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mso-fareast-language:ES-PE;} table.apaFirstRow {mso-style-name:apa; mso-table-condition:first-row; mso-style-priority:99; mso-style-unhide:no; mso-tstyle-border-bottom:.5pt solid windowtext;} </style> <![endif]--><!--[if gte mso 9]><xml> <o:shapedefaults v:ext="edit" spidmax="1026"/> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext="edit"> <o:idmap v:ext="edit" data="1"/> </o:shapelayout></xml><![endif]--> </head> <body lang=EN-US link=blue vlink=purple style='tab-interval:.5in;word-wrap: break-word'> <div class=WordSection1> <p class=MsoNormal align=center style='margin-right:-.05pt;text-align:center; line-height:115%'><b style='mso-bidi-font-weight:normal'><span lang=ES style='font-size:14.0pt;mso-bidi-font-size:11.0pt;line-height:115%'>Arquitectura de consolidación de la información para seguros de la salud mediante Big Data<o:p></o:p></span></b></p> <p class=MsoNormal align=center style='margin-right:-.05pt;text-align:center; line-height:115%'><i><span lang=ES style='font-size:9.0pt;mso-bidi-font-size: 7.5pt;line-height:115%;mso-bidi-font-weight:bold'><o:p>&nbsp;</o:p></span></i></p> <p class=MsoNormal align=center style='margin-right:-.05pt;text-align:center; line-height:115%'><i><span style='font-size:14.0pt;mso-bidi-font-size:11.0pt; line-height:115%;mso-ansi-language:EN-US;mso-bidi-font-weight:bold'>Information consolidation architecture for health insurance using Big Data</span></i><i><span style='font-size:9.0pt;mso-bidi-font-size:7.5pt;line-height:115%;mso-ansi-language: EN-US;mso-bidi-font-weight:bold'><o:p></o:p></span></i></p> <p class=MsoNormal align=center style='margin-right:-.05pt;text-align:center; line-height:115%'><i><span style='font-size:9.0pt;mso-bidi-font-size:7.5pt; line-height:115%;mso-ansi-language:EN-US;mso-bidi-font-weight:bold'><o:p>&nbsp;</o:p></span></i></p> <p class=MsoNormal align=center style='margin-right:-.05pt;text-align:center; line-height:115%'><i><span lang=ES style='font-size:14.0pt;mso-bidi-font-size: 11.0pt;line-height:115%;mso-bidi-font-weight:bold'>Arquitetura de consolidação de informações para planos de saúde usando Big Data<o:p></o:p></span></i></p> <p class=MsoNormal style='margin-right:-.05pt'><i style='mso-bidi-font-style: normal'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt'><o:p>&nbsp;</o:p></span></i></p> <p class=MsoNormal align=center style='margin-top:0in;margin-right:-.05pt; margin-bottom:4.0pt;margin-left:0in;text-align:center;text-indent:13.5pt'><i style='mso-bidi-font-style:normal'><span lang=ES-MX style='font-size:10.0pt; mso-bidi-font-size:7.5pt;color:#000009;mso-ansi-language:ES-MX'>José Zerega-Prado<span class=MsoFootnoteReference><span style='vertical-align:baseline'> </span></span></span></i><a style='mso-footnote-id:ftn1' href="#_ftn1" name="_ftnref1" title=""><span class=MsoFootnoteReference><i style='mso-bidi-font-style: normal'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt'><span style='mso-special-character:footnote'><![if !supportFootnotes]><span class=MsoFootnoteReference><b style='mso-bidi-font-weight:normal'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman";mso-ansi-language:ES;mso-fareast-language: ES;mso-bidi-language:ES'>[1]</span></b></span><![endif]></span></span></i></span></a><i style='mso-bidi-font-style:normal'><span lang=ES style='font-size:10.0pt; mso-bidi-font-size:11.0pt'>, </span></i><i style='mso-bidi-font-style:normal'><span lang=ES-MX style='font-size:10.0pt;mso-bidi-font-size:7.5pt;color:#000009; mso-ansi-language:ES-MX'>Joe Llerena-Izquierdo<span class=MsoFootnoteReference><span style='vertical-align:baseline'> </span></span></span></i><a style='mso-footnote-id: ftn2' href="#_ftn2" name="_ftnref2" title=""><span class=MsoFootnoteReference><i style='mso-bidi-font-style:normal'><span lang=ES style='font-size:10.0pt; mso-bidi-font-size:11.0pt'><span style='mso-special-character:footnote'><![if !supportFootnotes]><span class=MsoFootnoteReference><b style='mso-bidi-font-weight:normal'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman";mso-ansi-language:ES;mso-fareast-language: ES;mso-bidi-language:ES'>[2]</span></b></span><![endif]></span></span></i></span></a></p> <p class=MsoBodyText style='margin-right:-.05pt;text-align:justify;text-indent: 7.8pt'><b style='mso-bidi-font-weight:normal'><span lang=ES-MX style='mso-ansi-language:ES-MX'><o:p>&nbsp;</o:p></span></b></p> <p class=MsoNormal align=center style='margin-top:0in;margin-right:-.05pt; margin-bottom:1.0pt;margin-left:0in;text-align:center'><u><span lang=ES style='font-size:9.0pt;mso-bidi-font-size:11.0pt'>Recibido</span></u><span lang=ES style='font-size:9.0pt;mso-bidi-font-size:11.0pt'>: <span class=GramE>Febrero</span> 2022 <span style='mso-tab-count:6'>                                                                                                     </span><u>Aceptado</u>: Julio 2022<o:p></o:p></span></p> <p class=MsoBodyText align=center style='margin-right:-.05pt;text-align:center; text-indent:7.8pt'><b style='mso-bidi-font-weight:normal'><span lang=ES><o:p>&nbsp;</o:p></span></b></p> <p class=MsoBodyText style='margin-right:-.05pt;text-align:justify;text-indent: 7.8pt'><b style='mso-bidi-font-weight:normal'><span lang=ES>Resumen. - </span></b><span lang=ES>La identificación de los datos que están en varias fuentes de información y su consolidación para entregarla como útil se logra con Big Data. El objetivo general de este trabajo es desarrollar un diseño de arquitectura de consolidación de la información para seguros de la salud mediante Big Data. Para esta propuesta de investigación se utiliza el método empírico analítico, de tipo cuasi experimental con enfoque cuantitativo, mediante el análisis de referencias relevantes y especificación de los componentes de la arquitectura. Los resultados de esta investigación permiten categorizar diferentes arquitecturas computacionales para seguros de la salud mediante una revisión de literatura relevante, desarrollar un modelo de arquitectura de un sistema computacional para una empresa ecuatoriana de seguros de salud orientado a la consolidación de la información, y evaluar la metodología de estudio utilizada para establecer factores viables del modelo. El aporte de este trabajo permite determinar la aplicabilidad del modelo a empresas de seguros de salud nacionales o extranjeras mediante la contrastación de factores viables en una empresa específica del medio. Se concluye que las distintas fuentes de información o tipos de datos utilizados en el ámbito de los seguros de salud permiten conocer varias aristas del análisis de datos a través de una arquitectura en Big Data, además de obtener indicadores para mejorar la toma de decisiones; el 73% de los factores establecidos son viables en una empresa ecuatoriana de seguros de salud.</span></p> <p class=MsoBodyText style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt'><b style='mso-bidi-font-weight:normal'><span lang=ES>Palabras clave: </span></b><span lang=ES>Aplicaciones de Big Data; Salud y seguridad; Arquitectura de la información.</span></p> <p class=MsoBodyText style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt'><span lang=ES><o:p>&nbsp;</o:p></span></p> <p class=MsoNormal style='margin-top:6.75pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify;text-indent:7.6pt'><b style='mso-bidi-font-weight:normal'><i style='mso-bidi-font-style:normal'><span style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US'>Summary. - </span></i></b><i style='mso-bidi-font-style:normal'><span style='font-size: 10.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US'>The identification of data that is in various sources of information and its consolidation to deliver it as useful is achieved with Big Data. The overall objective of this work is to develop an information consolidation architecture design for health insurance using Big Data. For this research proposal, the analytical empirical method is used, of a quasi-experimental type with a quantitative approach, through the analysis of relevant references and specification of the architecture components. The results of this research allow categorizing different computational architectures for health insurance through a review of relevant literature, developing an architectural model of a computational system for an Ecuadorian health insurance company oriented to the consolidation of information, and evaluating the study methodology used to establish feasible factors of the model. The contribution of this work allows us to determine the applicability of the model to national or foreign health insurance companies by contrasting feasible factors in a specific company of the environment. It is concluded that the different sources of information or types of data used in the field of health insurance allow to know several edges of data analysis through a Big Data architecture, in addition to obtaining indicators to improve decision making; 73% of the established factors are viable in an Ecuadorian health insurance company.<o:p></o:p></span></i></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt'><b style='mso-bidi-font-weight:normal'><i style='mso-bidi-font-style:normal'><span style='font-size:10.0pt;mso-bidi-font-size: 11.0pt;mso-ansi-language:EN-US'>Keywords: </span></i></b><i style='mso-bidi-font-style: normal'><span style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language: EN-US'>Big Data applications; Health and safety; Information architecture.<o:p></o:p></span></i></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt'><i style='mso-bidi-font-style:normal'><span style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US'><o:p>&nbsp;</o:p></span></i></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><b><i style='mso-bidi-font-style:normal'><span lang=ES style='font-size:10.0pt; mso-bidi-font-size:11.0pt'>Resumo</span></i></b><i style='mso-bidi-font-style: normal'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt'>.<b> -</b> A identificação dos dados que estão em várias fontes de informação e sua consolidação para entregá-los como úteis é conseguida com o Big Data. O objetivo geral deste trabalho é desenvolver um projeto de arquitetura de consolidação da informação para planos de saúde por meio de Big Data. Para esta proposta de pesquisa, é utilizado o método empírico analítico, de tipo quase-experimental com abordagem quantitativa, por meio da análise de referências relevantes e especificação dos componentes da arquitetura. Os resultados desta pesquisa permitem a categorização de diferentes arquiteturas computacionais para seguros de saúde por meio de uma revisão da literatura relevante, o desenvolvimento de um modelo de arquitetura de um sistema de computador para uma seguradora de saúde equatoriana orientado para a consolidação de informações e a avaliação do metodologia de estudo utilizada para estabelecer os fatores viáveis do modelo. A contribuição deste trabalho permite determinar a aplicabilidade do modelo a seguradoras de saúde nacionais ou estrangeiras, comparando fatores viáveis em uma empresa específica no ambiente. Conclui-se que <span class=GramE>as diferentes</span> fontes de informação ou tipos de dados utilizados na área de seguros de saúde permitem conhecer vários aspectos da análise de dados por meio de uma arquitetura de Big Data, além de obter indicadores para melhorar a tomada de decisões; 73% dos fatores estabelecidos são viáveis em uma seguradora de saúde equatoriana.<o:p></o:p></span></i></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><i style='mso-bidi-font-style:normal'><span lang=ES style='font-size:10.0pt; mso-bidi-font-size:11.0pt'><o:p>&nbsp;</o:p></span></i></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><b><i style='mso-bidi-font-style:normal'><span lang=ES style='font-size:10.0pt; mso-bidi-font-size:11.0pt'>Palavras-chave:</span></i></b><i style='mso-bidi-font-style: normal'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt'> Aplicações de Big Data; Saúde e segurança; Arquitetura de informação.<o:p></o:p></span></i></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><i style='mso-bidi-font-style:normal'><span lang=ES style='font-size:10.0pt; mso-bidi-font-size:11.0pt'><o:p>&nbsp;</o:p></span></i></p> <i style='mso-bidi-font-style:normal'><span lang=ES style='font-size:10.0pt; mso-bidi-font-size:11.0pt;font-family:"Times New Roman",serif;mso-fareast-font-family: "Times New Roman";mso-ansi-language:ES;mso-fareast-language:ES;mso-bidi-language: ES'><br clear=all style='mso-special-character:line-break;page-break-before: always'> </span></i> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt'><o:p>&nbsp;</o:p></span></i></p> <p class=MsoNormal style='text-align:justify'><b style='mso-bidi-font-weight: normal'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt'>1. Introducción.- </span></b><span lang=ES style='font-size:10.0pt'>Los datos en el ámbito de la salud se generan desde diferentes fuentes y formatos; algunos de ellos son el historial médico, ingresos y salidas de hospital, medicamentos, tratamientos y prescripciones, datos de pacientes, imágenes, farmacias, laboratorios, sensores y compañías de seguros; estos datos están en continuo crecimiento, se generan con más velocidad y en varias diversidades </span><!--[if supportFields]><span lang=ES style='font-size:10.0pt'><span style='mso-element:field-begin; mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/FTC.2016.7821747&quot;,&quot;ISBN&quot;:&quot;978-1-5090-4171-8&quot;,&quot;abstract&quot;:&quot;The healthcare system consists of large volumes of data which are usually generated from diverse sources such as physicians' case notes, hospital admission notes, discharge summaries, pharmacies, insurance companies, medical imaging, laboratories, sensor based devices, genomics, social media as well as articles in medical journals. Healthcare data are however very complex and difficult to manage. This is as a result of the astronomical growth of healthcare data, the high speed at which these data are generated as well as the diversity of data types in healthcare. The capturing, storage, analysis and retrieval of health related data are rapidly shifting from paper based system towards digitization. However, the vast volume as well as the complexity of these data makes it difficult for the data to be processed and analyzed by traditional approaches and techniques. Consequently, technologies such as cloud computing and virtualization are now gradually used for processing massive data effectively and securely in healthcare. Hence, the healthcare system is swiftly becoming a big data industry. Thus, this paper examines the concept of big data in healthcare, its benefits and attendant challenges. This paper revealed that the fragmentation of healthcare data, ethical issues, usability issues as well as security and privacy issues are some of the factors impeding the successful implementation of big data in healthcare. This paper therefore suggests that ensuring security and privacy of healthcare data, the adoption of a standardized healthcare terminology, education strategy and the design of usable systems for processing large volumes of data are some of the ways of successfully implementing big data in healthcare.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Olaronke&quot;,&quot;given&quot;:&quot;Iroju&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Oluwaseun&quot;,&quot;given&quot;:&quot;Ojerinde&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;FTC&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;December&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;,&quot;12&quot;]]},&quot;page&quot;:&quot;1152-1157&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Big data in healthcare&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=cade217b-1b4b-4f36-9acb-f0050293fb8c&quot;,&quot;http://www.mendeley.com/documents/?uuid=f9a8e971-ce12-48e7-949c-68c14b07f6be&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[1]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[1]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[1]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES style='font-size:10.0pt'><span style='mso-no-proof:yes'>[1]</span></span><!--[if supportFields]><span lang=ES style='font-size:10.0pt'><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES style='font-size:10.0pt'><span style='mso-element:field-begin; mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1007/978-981-16-4126-8_43&quot;,&quot;ISBN&quot;:&quot;9789811641251&quot;,&quot;ISSN&quot;:&quot;21903026&quot;,&quot;abstract&quot;:&quot;Patients and doctors mistrust of data security in third-party information storage is a latent and relevant problem in the healthcare sector in Ecuador. This paper presents a data model to improve information security in Ecuador s healthcare sector by using Blockchain technology. A descriptive research is conducted using an exploratory-deductive, qualitative methodology to analyze relevant studies referencing Blockchain and Smart Contract technologies in the healthcare industry. The results of the study present the definition of participants and roles for a Blockchain-based healthcare model in Ecuador, a conceptual architecture to improve information security in the healthcare sector and impact factors. It is concluded that Blockchain technology allows sharing information securely; the proposed model is developed in the Hyperledger platform with 56% of information analyzed remaining as a private network.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Melendrez-Caicedo&quot;,&quot;given&quot;:&quot;Guillermo&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Llerena-Izquierdo&quot;,&quot;given&quot;:&quot;Joe&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;Smart Innovation, Systems and Technologies&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2022&quot;]]},&quot;page&quot;:&quot;479-489&quot;,&quot;publisher&quot;:&quot;Springer, Singapore&quot;,&quot;title&quot;:&quot;Secure Data Model for the Healthcare Industry in Ecuador Using Blockchain Technology&quot;,&quot;type&quot;:&quot;article-journal&quot;,&quot;volume&quot;:&quot;252&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=2a08585b-5a96-491d-8835-7d844c7f9081&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[2]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[2]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[2]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES style='font-size:10.0pt'><span style='mso-no-proof:yes'>[2]</span></span><!--[if supportFields]><span lang=ES style='font-size:10.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:10.0pt'>. A su vez las compañías en seguros de salud basan sus procedimientos y sistemas en datos generados por el sector de la salud </span><!--[if supportFields]><span lang=ES style='font-size:10.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/IDEA49133.2020.9170664&quot;,&quot;ISBN&quot;:&quot;9781728157184&quot;,&quot;abstract&quot;:&quot;At the present time, we can see around us everywhere big data is available like health sectors, insurance, share market, government organizations, social networking. Basically, big data refers to a huge amount of datasets and that dataset can be stored in the form of structured or unstructured format. Those datasets are producing in every second via a wide range of sources such as Facebook and Twitter. The big data has a basic three pillars- Volume (size), Variety (types) and Velocity (speed). Big data is a prediction platform where lots of dataset are available in the form of raw material, so we need an appropriate technique to process it and predict some valuable information for the present as well as future use. The sampling techniques are applicable to handle large datasets where all data sets are not possible to process at a time. In this situation, the sampling techniques play a very important role in the processing of big huge data. In this paper, we have proposed a parameter estimation model for big data to predict the mean size of large data by using the sampling technique. The proposed model can reduce the processing time as compare to the existing model because the sampling mechanism is to select small sample (n) from the entire datasets (N) and estimate the unknown parameters which are known as prediction results. Furthermore, we have presented the demonstration of the proposed algorithm on dynamic dataset and also compared result with simple random sampling (SRS) technique.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Alim&quot;,&quot;given&quot;:&quot;Abdul&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Shukla&quot;,&quot;given&quot;:&quot;Diwakar&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IDEA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2020&quot;]]},&quot;title&quot;:&quot;A parameter estimation model of big data setup&quot;,&quot;type&quot;:&quot;article-journal&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=119a424e-dbf6-4019-b123-f0d2b0245b2a&quot;,&quot;http://www.mendeley.com/documents/?uuid=21302b45-2345-488e-a333-4395ccb458d4&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[3]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[3]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[3]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES style='font-size:10.0pt'><span style='mso-no-proof:yes'>[3]</span></span><!--[if supportFields]><span lang=ES style='font-size:10.0pt'><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES style='font-size:10.0pt'><span style='mso-element:field-begin; mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;la Nube Toral Sarmiento&quot;,&quot;given&quot;:&quot;Adriana&quot;,&quot;non-dropping-particle&quot;:&quot;de&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Loaiza Martínez&quot;,&quot;given&quot;:&quot;María de Lourdes&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Llerena Izquierdo&quot;,&quot;given&quot;:&quot;Joe&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Ayala Carabajo&quot;,&quot;given&quot;:&quot;Raquel&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Torres Toukoumidis&quot;,&quot;given&quot;:&quot;Angel&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Romero-Rodríguez&quot;,&quot;given&quot;:&quot;Luis Miguel&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Aguaded&quot;,&quot;given&quot;:&quot;Ignacio&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Vega Ureta&quot;,&quot;given&quot;:&quot;Nino Tello&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Fuentes Espinoza&quot;,&quot;given&quot;:&quot;Pablo Gustavo&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Peñafiel Caicedo&quot;,&quot;given&quot;:&quot;Joseph Adrián&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;others&quot;,&quot;given&quot;:&quot;&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2018&quot;]]},&quot;title&quot;:&quot;4to. Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad. Memoria académica&quot;,&quot;type&quot;:&quot;webpage&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=a030a59b-8505-4ff9-8ed4-50f84cdd51c5&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[4]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[4]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[4]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES style='font-size:10.0pt'><span style='mso-no-proof:yes'>[4]</span></span><!--[if supportFields]><span lang=ES style='font-size:10.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:10.0pt'>. Los seguros de salud se aplican en diferentes sectores como, el sector público que atiende personas con financiamiento de impuestos,<span style='mso-spacerun:yes'>  </span>el sector privado que se financia con el cobro a los clientes, el seguro social que se financia con aportes de los empleados, y finalmente el seguro comunitario que se financia de impuestos y de la participación de la comunidad </span><!--[if supportFields]><span lang=ES style='font-size:10.0pt'><span style='mso-element:field-begin; mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICICCS.2016.7542360&quot;,&quot;ISBN&quot;:&quot;978-1-5090-2084-3&quot;,&quot;abstract&quot;:&quot;For the last century, the healthcare services and delivery including financing in India has been shrouded by life insurance challenges and importantly, shares key landmarks with general insurance. Many experts, practitioners and policy makers including researchers identified that, there is a big gap in the knowledge about innovations in public and private health financing and delivery. Apparent entails the necessity to alter the prevailing structure of the present health care services by applying Big Data Analytics. In this paper we'll discuss the various assorted implications, characteristics beside decrease in healthcare costs and how this new era of advanced and improved Data management is helping in Insurance Sector, more even we have to pay specific attention to the use cases which leads and drive new technology and eventually lead towards economic progress.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Gupta&quot;,&quot;given&quot;:&quot;Shalu&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Tripathi&quot;,&quot;given&quot;:&quot;Pooja&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICICCS&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;Iciccs&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;,&quot;2&quot;]]},&quot;page&quot;:&quot;64-69&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;An emerging trend of big data analytics with health insurance in India&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=b272e54d-383c-4792-9388-be7c3377813f&quot;,&quot;http://www.mendeley.com/documents/?uuid=700f2858-c030-4ca0-afdf-319814b0fa7e&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[5]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[5]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[5]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES style='font-size:10.0pt'><span style='mso-no-proof:yes'>[5]</span></span><!--[if supportFields]><span lang=ES style='font-size:10.0pt'><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES style='font-size:10.0pt'><span style='mso-element:field-begin; mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Ayala Carabajo&quot;,&quot;given&quot;:&quot;Raquel&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Llerena Izquierdo&quot;,&quot;given&quot;:&quot;Joe&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2017&quot;]]},&quot;title&quot;:&quot;Tercer Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad&quot;,&quot;type&quot;:&quot;article&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=a6d5f06b-56bf-4d78-bc0f-4ebff2af8819&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[6]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[6]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[6]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES style='font-size:10.0pt'><span style='mso-no-proof:yes'>[6]</span></span><!--[if supportFields]><span lang=ES style='font-size:10.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:10.0pt'>. <o:p></o:p></span></p> <p class=MsoBodyText style='margin-top:3.25pt;margin-right:0in;margin-bottom: 1.0pt;margin-left:0in;text-align:justify'><span lang=ES>En los seguros de salud se evidencia la usabilidad del Big Data (BD) por las siguientes razones: entender la expansión de enfermedades, tendencias en uso de medicamentos, mejorar los servicios médicos, aumentar el conocimiento médico desde diferentes perspectivas, suministrar información médica a los clientes, divisar cambios en prescripciones médicas, obtener sensores sociales, vincular las prescripciones entre medicamentos y enfermedades </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICDE.2019.00209&quot;,&quot;ISBN&quot;:&quot;978-1-5386-7474-1&quot;,&quot;ISSN&quot;:&quot;10844627&quot;,&quot;abstract&quot;:&quot;Understanding the spread of diseases and the use of medicines is of practical importance for various organizations, such as medical providers, medical payers, and national governments. This study aims to detect the change in the prescription trends and to identify its cause through an analysis of Medical Insurance Claims (MICs), which comprise the specifications of medical fees charged to health insurers. Our approach is two-fold. (1) We propose a latent variable model that simulates the medication behavior of physicians to accurately reproduce monthly prescription time series from the MIC data, where prescription links between the diseases and medicines are missing. (2) We apply a state space model with intervention variables to decompose the monthly prescription time series into different components including seasonality and structural changes. Using a large dataset consisting of 3.5-year MIC records, we conduct experiments to evaluate our approach in terms of accuracy, usefulness, and efficiency. We also demonstrate three applications for our medical analysis.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Umemoto&quot;,&quot;given&quot;:&quot;Kazutoshi&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Goda&quot;,&quot;given&quot;:&quot;Kazuo&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICDE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2019&quot;,&quot;4&quot;]]},&quot;page&quot;:&quot;1928-1939&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;A Prescription Trend Analysis using Medical Insurance Claim Big Data&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;2019-April&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=ef9430fc-799c-44e2-a2e4-3e111577d5ce&quot;,&quot;http://www.mendeley.com/documents/?uuid=516175e2-ee5d-4511-be25-ac7a6fb36be1&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[7]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[7]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[7]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[7]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Ayala Carabajo&quot;,&quot;given&quot;:&quot;Raquel&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Llerena Izquierdo&quot;,&quot;given&quot;:&quot;Joe&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;]]},&quot;title&quot;:&quot;Segundo Congreso Salesiano de Ciencia, Tecnología e Innovación para la Sociedad&quot;,&quot;type&quot;:&quot;article&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=6a3bf9c7-531e-4b7b-857a-de7c0674b4f6&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[8]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[8]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[8]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[8]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.18779/ingenio.v4i1.364&quot;,&quot;ISSN&quot;:&quot;2697-3642&quot;,&quot;abstract&quot;:&quot;Se presenta el desarrollo de una aplicación móvil para el control nutricional de la mujer gestante con riesgo de anemia ferropénica. Diseñado para registrar, consultar y sugerir las cantidades de hierro requeridas. La metodología de investigación es de corte cuantitativo, con enfoque empírico-analítico de nivel descriptivo. Se utiliza la técnica de la encuesta a una población de 200 mujeres en la ciudad de Lima, Perú. El alto índice de anemia en menores de cinco años, bajo peso del bebé al nacer, así como síntomas comunes que padece la mujer en gestación son factores determinantes que pueden mitigarse mediante el control mediado con el uso de la tecnología. Los resultados evidencian que el problema principal está en los registros de control de la alimentación de la mujer gestante muchas veces elaborados de forma tradicional. El 80% de las participantes indica que el aplicativo tiene un alto potencial en el área médica preventiva como apoyo al proceso de embarazo que evite el padecimiento de la anemia ferropénica considerada un problema de salud en Perú.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Llerena-Izquierdo&quot;,&quot;given&quot;:&quot;Joe&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Merino-Lazo&quot;,&quot;given&quot;:&quot;María&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;Revista InGenio&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;page&quot;:&quot;17-26&quot;,&quot;title&quot;:&quot;Aplicación móvil de control nutricional para prevención de la anemia ferropénica en la mujer gestante&quot;,&quot;type&quot;:&quot;article-journal&quot;,&quot;volume&quot;:&quot;4&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=37b51ed7-e145-4cf6-8f17-3443e3b1a22f&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[9]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[9]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[9]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[9]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>, búsqueda en aumentar la experiencia del cliente, mejorar los comentarios de los clientes en los procesos y redes sociales </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICICCS.2016.7542360&quot;,&quot;ISBN&quot;:&quot;978-1-5090-2084-3&quot;,&quot;abstract&quot;:&quot;For the last century, the healthcare services and delivery including financing in India has been shrouded by life insurance challenges and importantly, shares key landmarks with general insurance. Many experts, practitioners and policy makers including researchers identified that, there is a big gap in the knowledge about innovations in public and private health financing and delivery. Apparent entails the necessity to alter the prevailing structure of the present health care services by applying Big Data Analytics. In this paper we'll discuss the various assorted implications, characteristics beside decrease in healthcare costs and how this new era of advanced and improved Data management is helping in Insurance Sector, more even we have to pay specific attention to the use cases which leads and drive new technology and eventually lead towards economic progress.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Gupta&quot;,&quot;given&quot;:&quot;Shalu&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Tripathi&quot;,&quot;given&quot;:&quot;Pooja&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICICCS&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;Iciccs&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;,&quot;2&quot;]]},&quot;page&quot;:&quot;64-69&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;An emerging trend of big data analytics with health insurance in India&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=700f2858-c030-4ca0-afdf-319814b0fa7e&quot;,&quot;http://www.mendeley.com/documents/?uuid=b272e54d-383c-4792-9388-be7c3377813f&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[5]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[5]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[5]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[5]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Soto Eras&quot;,&quot;given&quot;:&quot;Wilmer Moisés&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;title&quot;:&quot;Desarrollo del portal web de la fundación nuestra Señora del Cisne para la gestión de servicios en el Cantón Durán&quot;,&quot;type&quot;:&quot;webpage&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=cab06af6-41e3-45dc-af37-12c079c6dd66&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[10]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[10]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[10]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[10]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>, detectar fraudes generales en los reclamos </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICBDAA.2017.8284114&quot;,&quot;ISBN&quot;:&quot;978-1-5386-0790-9&quot;,&quot;abstract&quot;:&quot;Deliberate cheating by concealing and omitting facts while claiming from health insurance providers is considered as one of fraudulent activities in the health insurance domain which has led to significant amount of monetary loss to the providers. In view of the above, careful scanning of the submitted claim documents need to be conducted by the insurance companies in order to spot any discrepancy that indicates fraud. For this purpose, manual detection is neither easy nor practical as the claim documents received are plentiful and for diverse medical treatments. Hence, this paper shares the initial stage of our study which is aimed to propose an approach for detecting fraudulent health insurance claims by identifying correlation or association between some of the attributes on the claim documents. With the application of a data mining technique of association rules, this study advocates that the successful determination of correlated attributes can adequately address the discrepancies of data in fraudulent claims and thus reduce fraud in health insurance.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Kareem&quot;,&quot;given&quot;:&quot;Saba&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Binti Ahmad&quot;,&quot;given&quot;:&quot;Rohiza&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICBDA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2017&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;99-104&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Framework for the identification of fraudulent health insurance claims&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;2018-Janua&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=2fb1561c-82a1-4901-b617-bba095bc87cd&quot;,&quot;http://www.mendeley.com/documents/?uuid=ffe5af9a-8128-4960-acdc-97af8c3f7383&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[11]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[11]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[11]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[11]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>, entre otros. Así Big Data introduce grandes mejoras en el área del seguro de salud como: lealtad y retención de clientes, entender la conducta de los clientes, buscar oportunidades con otros clientes, entender la conducta de los canales de ventas en seguros, aumento en venta de seguros de salud, conducta de los beneficiarios de las pólizas, búsqueda de nuevas redes de personas, reducción de costos y precios en la compañía, detectar fraudes en los reclamos, selección inmediata de reclamos interrelacionadas entre compañías y mejoras en los proceso de reclamaciones </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICICCS.2016.7542360&quot;,&quot;ISBN&quot;:&quot;978-1-5090-2084-3&quot;,&quot;abstract&quot;:&quot;For the last century, the healthcare services and delivery including financing in India has been shrouded by life insurance challenges and importantly, shares key landmarks with general insurance. 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The most widely used smartphone application is social media, around 60% of Indonesia's population uses social media. The number of social media users in Indonesia opens up opportunities to analyze these social media data. Social media data mining, commonly referred to as text mining, can help stakeholders implement a data driven policy. In this paper will discuss the public perception on social media related to the issue of rising fees national health insurance or Badan Penyelenggara Jaminan Sosial (BPJS Kesehatan). The social media analyzed is Twitter because it can provide a comprehensive sentiment and discussion of an issue. We used Drone Emprit system to analyze social media data, Drone Emprit is a big data system that captures and analyzes discussion and sentiments on social media platforms, in this case Twitter. The data analyzed are all conversations that took place over 60 days in the time span of 22 September to 22 November 2019. 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This study aims to detect the change in the prescription trends and to identify its cause through an analysis of Medical Insurance Claims (MICs), which comprise the specifications of medical fees charged to health insurers. Our approach is two-fold. (1) We propose a latent variable model that simulates the medication behavior of physicians to accurately reproduce monthly prescription time series from the MIC data, where prescription links between the diseases and medicines are missing. (2) We apply a state space model with intervention variables to decompose the monthly prescription time series into different components including seasonality and structural changes. Using a large dataset consisting of 3.5-year MIC records, we conduct experiments to evaluate our approach in terms of accuracy, usefulness, and efficiency. We also demonstrate three applications for our medical analysis.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Umemoto&quot;,&quot;given&quot;:&quot;Kazutoshi&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Goda&quot;,&quot;given&quot;:&quot;Kazuo&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICDE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2019&quot;,&quot;4&quot;]]},&quot;page&quot;:&quot;1928-1939&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;A Prescription Trend Analysis using Medical Insurance Claim Big Data&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;2019-April&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=516175e2-ee5d-4511-be25-ac7a6fb36be1&quot;,&quot;http://www.mendeley.com/documents/?uuid=ef9430fc-799c-44e2-a2e4-3e111577d5ce&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[7]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[7]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[7]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[7]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;abstract&quot;:&quot;El trabajo pretende identificar el uso de método deductivo y la investigación exploratoria. Para el desarrollo de este articulo académico se realizó un estudio a la metodología MAGERIT que protege la información en su confidencialidad y disponibilidad dando garantía a la seguridad del sistema, incluyendo a los procesos de las organizaciones públicas y un mapeo sistemático.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Terán Terranova&quot;,&quot;given&quot;:&quot;Yaritza Julieth&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;title&quot;:&quot;Seguridad en la Gestión de la información para las organizaciones públicas desde el enfoque ISO/IEC 2700: un Mapeo Sistemático&quot;,&quot;type&quot;:&quot;webpage&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=2964fc94-7952-4859-b94c-eb1fbd2f2570&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[15]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[15]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[15]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[15]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. Las existentes leyes locales y normas generales para uso correcto de los datos de salud, deben ser cumplidas por médicos y proveedores de salud, además los cambios en las regulaciones se aplican a los procedimientos y sistemas informáticos; es necesario recordar que la adherencia en normas de salud es esencial en la gestión de información privada de los pacientes, y en este escenario los datos generados por la pandemia COVID-19 también deben respetarse </span><!--[if supportFields]><span lang=ES><span style='mso-element: field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/BigDataSecurityHPSCIDS52275.2021.00013&quot;,&quot;ISBN&quot;:&quot;978-1-6654-3927-5&quot;,&quot;abstract&quot;:&quot;As healthcare organizations adopt cloud-based services to manage their patient data, compliance with the rules and policies of the Health Insurance Portability and Accountability Act (HIPAA) regulation becomes increasingly complex. At present, HIPAA rules are available only in large textual format and require significant human effort to implement in the Health IT systems. Moreover, every change in the regulation, like the recent relaxation in telehealth policy due to the COVID-19 pandemic, has to be manually implemented in the IT system. We have developed a semantically rich Knowledge graph, using Semantic Web technologies to represent HIPAA rules in a machine-processable format. This will significantly help in automatically reasoning of HIPAA policies. In this paper, we describe our design along with the results of our study of the current status of research on HIPAA ontology. We have validated our design against use cases defined by the US Department of Health and Human Services (HHS). This knowledge graph can be integrated with existing healthcare systems to provide automated compliance with HIPAA policies.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Kim&quot;,&quot;given&quot;:&quot;Dae-young&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Joshi&quot;,&quot;given&quot;:&quot;Karuna P.&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IEEE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;,&quot;5&quot;]]},&quot;page&quot;:&quot;7-12&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;A Semantically Rich Knowledge Graph&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=5334d33d-c800-4745-831f-fcbb4c8cb6f5&quot;,&quot;http://www.mendeley.com/documents/?uuid=23211789-1944-4015-86f8-4e692abc9680&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[16]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[16]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[16]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[16]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;abstract&quot;:&quot;La nube es un concepto muy poderoso que está bajo ataque por el volumen de datos almacenados y servicios importantes para los consumidores. Tiene muchas ventajas para la tecnología una de ella es la capacidad para transmitir grandes cantidades de datos donde debe existir una gran seguridad. Se analizaron varios artículos para realizar una amplia búsqueda.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Guaigua Bucheli&quot;,&quot;given&quot;:&quot;Cynthia Janeth&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;title&quot;:&quot;Algoritmos de seguridad para mitigar riesgos de datos en la nube: un mapeo sistemático&quot;,&quot;type&quot;:&quot;webpage&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=fa52dc6f-d1de-4c3c-9561-836686e87f58&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[17]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[17]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[17]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[17]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Ponce Larreategui&quot;,&quot;given&quot;:&quot;Jimmy Gregorio&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;title&quot;:&quot;Indicadores de compromiso (IOC) para detección de amenazas en la seguridad informática con enfoque en el código malicioso&quot;,&quot;type&quot;:&quot;thesis&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=25d0f17e-2720-43ea-a57b-57e25bd4dd95&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[18]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[18]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[18]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[18]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>.</span></p> <p class=MsoBodyText style='margin-top:3.25pt;margin-right:0in;margin-bottom: 1.0pt;margin-left:0in;text-align:justify'><span lang=ES>Ecuador tiene 21 compañías privadas en seguros de salud legalmente constituidas bajo control de la entidad de la Superintendencia de Compañías, Valores y Seguros. Además, este tipo de compañía de seguros de salud es llamada medicina pre pagada debido a que estas compañías ofrecen los seguros en forma directa o por intermediarios del sistema de seguros existente en el medio, así los clientes contratan este servicio por un pago anual </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;URL&quot;:&quot;https://appscvsmovil.supercias.gob.ec/portaldeinformacion/cias_medicina_prepagada.zul&quot;,&quot;accessed&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;,&quot;9&quot;,&quot;27&quot;]]},&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Superintendencia-de-Compañias-Valores-y-Seguros&quot;,&quot;given&quot;:&quot;&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;Listado&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;page&quot;:&quot;1&quot;,&quot;title&quot;:&quot;Companias de Medicina Prepagada Ecuador&quot;,&quot;type&quot;:&quot;webpage&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=33e26db7-53b7-46a4-8ffb-96579f21e556&quot;,&quot;http://www.mendeley.com/documents/?uuid=3729ee9e-eea8-48ba-a5fa-c599853e5f96&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[19]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[19]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[19]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[19]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;abstract&quot;:&quot;El permanente crecimiento y el auge en las redes de internet y tecnologías permiten la obtención de miles de datos día con día, los cuales se almacenan en bases de datos con grandes capacidades, esto las pone en riesgo de sufrir ataques por ciberdelincuentes. Este trabajo tiene como objetivo identificar qué mecanismos de defensa existen para mantener la seguridad de los datos almacenados, se pretende efectuar un repaso de las tecnologías de seguridad usadas para mitigar los riesgos y amenazas para obtener un registro de los trabajos y estudios orientados a mantener la seguridad.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Aguirre Sánchez&quot;,&quot;given&quot;:&quot;Maitté Jazmín&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;title&quot;:&quot;Tecnologías de Seguridad en Bases de Datos: Revisión Sistemática&quot;,&quot;type&quot;:&quot;webpage&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=f5538192-2858-4fbc-a462-8067f85bb5b9&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[20]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[20]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[20]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[20]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>; además existe un servicio público del gobierno ecuatoriano que reembolsa dinero a las personas en caso de accidentes, por ejemplo el seguro de tránsito </span><!--[if supportFields]><span lang=ES><span style='mso-element: field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;URL&quot;:&quot;https://www.gob.ec/sppat/tramites/solicitud-pago-proteccion-gastos-medicos-victimas-accidentes-transito&quot;,&quot;accessed&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;,&quot;9&quot;,&quot;27&quot;]]},&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Ecuatoriano&quot;,&quot;given&quot;:&quot;Gobierno&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;page&quot;:&quot;1&quot;,&quot;title&quot;:&quot;Servicio Publico para pagos de accidentes de transito&quot;,&quot;type&quot;:&quot;webpage&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=81279e48-34c8-4f1d-a8d3-d79f0b2343ed&quot;,&quot;http://www.mendeley.com/documents/?uuid=0c6db6e1-32a2-4e11-ad52-3d8e6363eb90&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[21]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[21]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[21]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[21]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. Este estudio se centra en los datos a nivel general de los reclamos a compañías en seguro de salud. Los sistemas en seguros de salud gestionan la adquisición de datos por medio de: solicitudes de seguro individual, declaración del médico, formulario de reclamos, anexos de explicaciones médicas, anexo de datos de los dependientes, formulario de pago, anexo de cambios o actualización. Los datos que existen en las compañías de seguros de salud tienen características heterogéneas, es decir están en un formato estructurado (bases de datos, hojas electrónicas) y en formato no estructurado (procesadores de palabras, correos electrónicos, archivos portables «pdf», redes sociales). Desde el área de salud, cada día se generan y capturan datos, y están en revisión desde sistemas que pertenecen a las compañías en seguro de salud, y estos grandes datos necesitan procesos, herramientas y tecnología para una organización sistemática </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICICCS.2016.7542360&quot;,&quot;ISBN&quot;:&quot;978-1-5090-2084-3&quot;,&quot;abstract&quot;:&quot;For the last century, the healthcare services and delivery including financing in India has been shrouded by life insurance challenges and importantly, shares key landmarks with general insurance. Many experts, practitioners and policy makers including researchers identified that, there is a big gap in the knowledge about innovations in public and private health financing and delivery. Apparent entails the necessity to alter the prevailing structure of the present health care services by applying Big Data Analytics. In this paper we'll discuss the various assorted implications, characteristics beside decrease in healthcare costs and how this new era of advanced and improved Data management is helping in Insurance Sector, more even we have to pay specific attention to the use cases which leads and drive new technology and eventually lead towards economic progress.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Gupta&quot;,&quot;given&quot;:&quot;Shalu&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Tripathi&quot;,&quot;given&quot;:&quot;Pooja&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICICCS&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;Iciccs&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;,&quot;2&quot;]]},&quot;page&quot;:&quot;64-69&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;An emerging trend of big data analytics with health insurance in India&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=700f2858-c030-4ca0-afdf-319814b0fa7e&quot;,&quot;http://www.mendeley.com/documents/?uuid=b272e54d-383c-4792-9388-be7c3377813f&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[5]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[5]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[5]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[5]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;abstract&quot;:&quot;Se evidencia la problemática de seguridad en el sector de telecomunicaciones ya que al ser proveedores de este tipo de servicio son blancos perfectos de ataques DoS, por lo tanto, en este artículo se estudió un modelo de tecnología de la información para la creación de un plan de mitigación de riesgos de protección en las entidades de telecomunicaciones.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Chévez Morán&quot;,&quot;given&quot;:&quot;María José&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;title&quot;:&quot;Estudio de los patrones de seguridad para la atenuación de las irregularidades, las debilidades y amenazas en empresas de servicios de telecomunicaciones&quot;,&quot;type&quot;:&quot;webpage&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=1400e043-0b25-4b04-b0d1-d16a8667d223&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[22]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[22]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[22]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[22]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;abstract&quot;:&quot;Se analizó los modelos Blockchain para la seguridad de la base de datos y la detección de anomalías en las referencias para mitigar los riesgos de ataques en los servidores de almacenamiento. El problema es que algunos sistemas en Latinoamérica no se manejan con políticas de seguridad de la información necesaria, los proyectos no son muy bien financiados por lo que conlleva un problema a futuro; modelos que están en funcionamiento en las bases de datos de las organizaciones muestran vulnerabilidades por lo que muestran una brecha a los atacantes cibernéticos una oportunidad de realizar acciones que perjudique a un país.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Escalante Quimis&quot;,&quot;given&quot;:&quot;Omar Alexander&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;title&quot;:&quot;Prototipo de sistema de seguridad de base de datos en organizaciones públicas para mitigar ataques cibernéticos en Latinoamérica&quot;,&quot;type&quot;:&quot;webpage&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=d2a80d6b-2764-45e5-8920-f914ca0fb9ae&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[23]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[23]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[23]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[23]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. </span></p> <p class=MsoBodyText style='margin-top:3.25pt;margin-right:0in;margin-bottom: 1.0pt;margin-left:0in;text-align:justify'><span lang=ES>En las compañías de seguros de salud, el trabajo sobre análisis de datos es complejo, la confirmación de discrepancias toma mucho tiempo, la detección de documentos fraudulentos es difícil de detectar, existe gran cantidad de documentos de reclamos, se evidencia diversidad en tratamientos y prescripciones médicas, es decir los datos son abundantes </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICBDAA.2017.8284114&quot;,&quot;ISBN&quot;:&quot;978-1-5386-0790-9&quot;,&quot;abstract&quot;:&quot;Deliberate cheating by concealing and omitting facts while claiming from health insurance providers is considered as one of fraudulent activities in the health insurance domain which has led to significant amount of monetary loss to the providers. In view of the above, careful scanning of the submitted claim documents need to be conducted by the insurance companies in order to spot any discrepancy that indicates fraud. For this purpose, manual detection is neither easy nor practical as the claim documents received are plentiful and for diverse medical treatments. Hence, this paper shares the initial stage of our study which is aimed to propose an approach for detecting fraudulent health insurance claims by identifying correlation or association between some of the attributes on the claim documents. With the application of a data mining technique of association rules, this study advocates that the successful determination of correlated attributes can adequately address the discrepancies of data in fraudulent claims and thus reduce fraud in health insurance.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Kareem&quot;,&quot;given&quot;:&quot;Saba&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Binti Ahmad&quot;,&quot;given&quot;:&quot;Rohiza&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICBDA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2017&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;99-104&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Framework for the identification of fraudulent health insurance claims&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;2018-Janua&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=ffe5af9a-8128-4960-acdc-97af8c3f7383&quot;,&quot;http://www.mendeley.com/documents/?uuid=2fb1561c-82a1-4901-b617-bba095bc87cd&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[11]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[11]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[11]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[11]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;abstract&quot;:&quot;Las ciberamenzas son riesgos para las entidades públicas buscan tener una infraestructura con una sólida ciberseguridad para así poder brindar de manera correcta sus servicios. Dado que en Ecuador tiene vulnerabilidades en sus procesos administrativos, al realizar este análisis se busca establecer ciertas políticas de seguridad para que se sólido y fiable y así poder evitar pérdidas de información, manipulación de información por terceros y corrupción con fines criminales para así estabilizar o destruir la parte administrativa de las organizaciones públicas.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Coello Ochoa&quot;,&quot;given&quot;:&quot;Ingrid Nicole&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;title&quot;:&quot;Análisis de ciberataques en organizaciones públicas del Ecuador y sus impactos administrativos&quot;,&quot;type&quot;:&quot;webpage&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=24de921f-0641-49df-9af4-0c731a51e8d4&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[24]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[24]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[24]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[24]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Orozco Bonilla&quot;,&quot;given&quot;:&quot;Christian Andrés&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;title&quot;:&quot;Estrategias algorítmicas orientadas a la ciberseguridad: Un mapeo sistemático&quot;,&quot;type&quot;:&quot;thesis&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=11f80e50-5606-4019-8de0-a98a9cb21a71&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[25]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[25]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[25]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[25]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. En los sistemas de seguros de salud, los clientes solicitan el reembolso de los pagos en atención médica y medicina a través de formularios que poseen datos clínicos y detallados. </span></p> <p class=MsoBodyText style='margin-top:3.25pt;margin-right:0in;margin-bottom: 1.0pt;margin-left:0in;text-align:justify'><span lang=ES>Se propone un modelo computacional para una empresa ecuatoriana en seguros de salud que gestione los datos heterogéneos y los consolide en información, para el modelo propuesto se debe exceptuar aquellos datos que provienen de pruebas médicas, imágenes o radiografías debido a que éstos, son datos confidenciales del paciente. El objetivo general plantea desarrollar un diseño de arquitectura de consolidación de la información para seguros de la salud mediante Big Data.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><b style='mso-bidi-font-weight:normal'><span lang=ES style='mso-bidi-font-size: 11.0pt'>2. Metodología. - </span></b><span lang=ES>Para la propuesta de diseño de una arquitectura de consolidación de la información se utiliza una metodología <span class=GramE>empírico analítica</span>, de tipo cuasi experimental con enfoque cuantitativo para el análisis de las referencias y propuesta de los componentes de la arquitectura. Se revisan las bibliotecas virtuales IEEExplore, Scopus, ACM, WoS entre otras. Se identifican y seleccionan los documentos con base teórica de propuestas sobre Big Data y Health Insurance. Se analizan los componentes de las arquitecturas en Big Data en los artículos científicos relevantes. Se escogen artículos científicos de alto impacto para el análisis de los elementos necesarios que permitan proponer una arquitectura de Big Data en el área de seguros de salud para una empresa en el contexto ecuatoriano. Se elabora un modelo de la arquitectura, su diagrama y debida explicación. Finalmente se evalúa la metodología utilizada para la factibilidad y aplicabilidad en una empresa específica en el medio.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>En el desarrollo del modelo de arquitectura se utiliza una metodología iterativa </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/BigData.2015.7363798&quot;,&quot;ISBN&quot;:&quot;978-1-4799-9926-2&quot;,&quot;abstract&quot;:&quot;Big Data constitutes an opportunity for companies to empower their analysis. However, at the moment there is no standard way for approaching Big Data projects. This, coupled with the complex nature of Big Data, is the cause that many Big Data projects fail or rarely obtain the expected return of investment. In this paper, we present a methodology to tackle Big Data projects in a systematic way, avoiding the aforementioned problems. To this end, we review the state of the art, identifying the most prominent problems surrounding Big Data projects, best practices and methods. Then, we define a methodology describing step by step how these techniques could be applied and combined in order to tackle the problems identified and increase the success rate of Big Data projects.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Tardio&quot;,&quot;given&quot;:&quot;Roberto&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Mate&quot;,&quot;given&quot;:&quot;Alejandro&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IEEE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2015&quot;,&quot;10&quot;]]},&quot;page&quot;:&quot;545-550&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;An iterative methodology for big data management&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=b4a1c6ca-8089-4033-a168-5d5f9672be5d&quot;,&quot;http://www.mendeley.com/documents/?uuid=0a528cd7-63f3-4ac0-aa71-72fdb844a31c&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[26]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[26]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[26]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[26]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES> que ayuda a gestionar, examinar y visualizar los datos, la metodología contiene buenas prácticas y técnicas coordinadas que consiste en 5 fases: i) Definir etapas de los datos, ii) Gestionar las fuentes de datos, iii) Valorizar los datos, iv) Selección del almacén de datos, v) Implementación visual del Big Data. En la figura I se muestran las fases, en un proceso iterativo, debido a que se pueden realizar y repetir cada vez que existan más hallazgos de datos, de acuerdo con esto, el diseño de la arquitectura se aplica sobre datos de una empresa ecuatoriana de seguros de salud.</span></p> <div align=center> <table class=TableNormal1 border=0 cellspacing=0 cellpadding=0 width=503 style='border-collapse:collapse;mso-table-layout-alt:fixed;mso-yfti-tbllook: 480;mso-padding-alt:0in 0in 0in 0in'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;mso-yfti-lastrow:yes; height:109.9pt'> <td width=503 valign=top style='width:376.95pt;padding:0in 0in 0in 0in; height:109.9pt'> <div align=center> <table class=MsoTableGrid border=0 cellspacing=0 cellpadding=0 style='border-collapse:collapse;mso-table-layout-alt:fixed;border:none; mso-yfti-tbllook:1184;mso-padding-alt:0in 5.4pt 0in 5.4pt;mso-border-insideh: none;mso-border-insidev:none'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;height:71.2pt'> <td width=530 valign=top style='width:397.25pt;padding:0in 5.4pt 0in 5.4pt; height:71.2pt'> <p class=TableParagraph align=center style='margin-top:.05pt;text-align: center'><span lang=ES-EC style='mso-ansi-language:ES-EC;mso-fareast-language: ES-EC;mso-bidi-language:AR-SA;mso-no-proof:yes'><!--[if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> <v:stroke joinstyle="miter"/> <v:formulas> <v:f eqn="if lineDrawn pixelLineWidth 0"/> <v:f eqn="sum @0 1 0"/> <v:f eqn="sum 0 0 @1"/> <v:f eqn="prod @2 1 2"/> <v:f eqn="prod @3 21600 pixelWidth"/> <v:f eqn="prod @3 21600 pixelHeight"/> <v:f eqn="sum @0 0 1"/> <v:f eqn="prod @6 1 2"/> <v:f eqn="prod @7 21600 pixelWidth"/> <v:f eqn="sum @8 21600 0"/> <v:f eqn="prod @7 21600 pixelHeight"/> <v:f eqn="sum @10 21600 0"/> </v:formulas> <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/> <o:lock v:ext="edit" aspectratio="t"/> </v:shapetype><v:shape id="Imagen_x0020_7" o:spid="_x0000_i1029" type="#_x0000_t75" style='width:335pt;height:105pt;visibility:visible;mso-wrap-style:square'> <v:imagedata src="02_MIIUM23-03_Ecuador_Format.fld/image001.jpg" o:title=""/> </v:shape><![endif]--><![if !vml]><img width=335 height=105 src="http://www4.um.edu.uy/mailings/Imagenes/OJS_ING/Llerena001.jpg" v:shapes="Imagen_x0020_7"><![endif]></span><span lang=ES style='font-size:5.5pt;mso-bidi-font-size:11.0pt'><o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:1;mso-yfti-lastrow:yes;height:15.4pt'> <td width=530 valign=top style='width:397.25pt;padding:0in 5.4pt 0in 5.4pt; height:15.4pt'> <p class=TableParagraph align=center style='margin-top:.05pt;margin-right: .1in;margin-bottom:0in;margin-left:.1in;margin-bottom:.0001pt;text-align: center'><i><span lang=ES style='font-size:9.0pt;font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman"'>Figura I.- Metodología para desarrollo de Big Data<o:p></o:p></span></i></p> </td> </tr> </table> </div> </td> </tr> </table> </div> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><b style='mso-bidi-font-weight:normal'><span lang=ES>3. Revisión de la literatura. -<o:p></o:p></span></b></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><b style='mso-bidi-font-weight:normal'><span lang=ES>3.1. Big Data y conceptos generales. - </span></b><span lang=ES>Big Data es una colección de conjuntos de datos en considerables volúmenes. Medir o localizar problemas para una organización es complicado por la variedad de datos que tiene. Con Big Data se abren posibilidades de aplicar herramientas tecnológicas, procesos y métodos para gestionar el conjuntos de datos y luego su posterior almacenamiento </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICICCS.2016.7542360&quot;,&quot;ISBN&quot;:&quot;978-1-5090-2084-3&quot;,&quot;abstract&quot;:&quot;For the last century, the healthcare services and delivery including financing in India has been shrouded by life insurance challenges and importantly, shares key landmarks with general insurance. Many experts, practitioners and policy makers including researchers identified that, there is a big gap in the knowledge about innovations in public and private health financing and delivery. Apparent entails the necessity to alter the prevailing structure of the present health care services by applying Big Data Analytics. In this paper we'll discuss the various assorted implications, characteristics beside decrease in healthcare costs and how this new era of advanced and improved Data management is helping in Insurance Sector, more even we have to pay specific attention to the use cases which leads and drive new technology and eventually lead towards economic progress.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Gupta&quot;,&quot;given&quot;:&quot;Shalu&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Tripathi&quot;,&quot;given&quot;:&quot;Pooja&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICICCS&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;Iciccs&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;,&quot;2&quot;]]},&quot;page&quot;:&quot;64-69&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;An emerging trend of big data analytics with health insurance in India&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=700f2858-c030-4ca0-afdf-319814b0fa7e&quot;,&quot;http://www.mendeley.com/documents/?uuid=b272e54d-383c-4792-9388-be7c3377813f&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[5]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[5]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[5]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[5]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Muñoz Campuzano&quot;,&quot;given&quot;:&quot;Peter Steeven&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;title&quot;:&quot;Modelos de seguridad para prevenir riesgos de ataques Informáticos: Una revisión sistemática&quot;,&quot;type&quot;:&quot;thesis&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=5a0a92b9-0e4d-4d20-8dcb-c665d89d5eca&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[27]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[27]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[27]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[27]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. El conjunto de datos se almacena en formato estructurado o no estructurado y los datos disponibles sirven como plataforma de predicción </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/IDEA49133.2020.9170664&quot;,&quot;ISBN&quot;:&quot;9781728157184&quot;,&quot;abstract&quot;:&quot;At the present time, we can see around us everywhere big data is available like health sectors, insurance, share market, government organizations, social networking. Basically, big data refers to a huge amount of datasets and that dataset can be stored in the form of structured or unstructured format. Those datasets are producing in every second via a wide range of sources such as Facebook and Twitter. The big data has a basic three pillars- Volume (size), Variety (types) and Velocity (speed). Big data is a prediction platform where lots of dataset are available in the form of raw material, so we need an appropriate technique to process it and predict some valuable information for the present as well as future use. The sampling techniques are applicable to handle large datasets where all data sets are not possible to process at a time. In this situation, the sampling techniques play a very important role in the processing of big huge data. In this paper, we have proposed a parameter estimation model for big data to predict the mean size of large data by using the sampling technique. The proposed model can reduce the processing time as compare to the existing model because the sampling mechanism is to select small sample (n) from the entire datasets (N) and estimate the unknown parameters which are known as prediction results. Furthermore, we have presented the demonstration of the proposed algorithm on dynamic dataset and also compared result with simple random sampling (SRS) technique.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Alim&quot;,&quot;given&quot;:&quot;Abdul&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Shukla&quot;,&quot;given&quot;:&quot;Diwakar&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IDEA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2020&quot;]]},&quot;title&quot;:&quot;A parameter estimation model of big data setup&quot;,&quot;type&quot;:&quot;article-journal&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=21302b45-2345-488e-a333-4395ccb458d4&quot;,&quot;http://www.mendeley.com/documents/?uuid=119a424e-dbf6-4019-b123-f0d2b0245b2a&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[3]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[3]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[3]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[3]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Vera Navas&quot;,&quot;given&quot;:&quot;Nelson Alexander&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;]]},&quot;title&quot;:&quot;Modelo de seguridad informática para riesgos de robo de información por el uso de las redes sociales&quot;,&quot;type&quot;:&quot;thesis&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=c61daf86-8736-484e-8bf1-2f4392c482ff&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[28]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[28]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[28]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[28]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>De acuerdo a Alim y Abdul </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/IDEA49133.2020.9170664&quot;,&quot;ISBN&quot;:&quot;9781728157184&quot;,&quot;abstract&quot;:&quot;At the present time, we can see around us everywhere big data is available like health sectors, insurance, share market, government organizations, social networking. 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The proposed model can reduce the processing time as compare to the existing model because the sampling mechanism is to select small sample (n) from the entire datasets (N) and estimate the unknown parameters which are known as prediction results. Furthermore, we have presented the demonstration of the proposed algorithm on dynamic dataset and also compared result with simple random sampling (SRS) technique.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Alim&quot;,&quot;given&quot;:&quot;Abdul&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Shukla&quot;,&quot;given&quot;:&quot;Diwakar&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IDEA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2020&quot;]]},&quot;title&quot;:&quot;A parameter estimation model of big data setup&quot;,&quot;type&quot;:&quot;article-journal&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=21302b45-2345-488e-a333-4395ccb458d4&quot;,&quot;http://www.mendeley.com/documents/?uuid=119a424e-dbf6-4019-b123-f0d2b0245b2a&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[3]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[3]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[3]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[3]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>, Big Data tiene tres características principales: volumen, velocidad y variedad. Debido a que, en volumen, los datos médicos se duplican anualmente. En velocidad, el área de salud de cualquier departamento, redes o equipo genera más datos que otras áreas o sectores. En variedad, los datos generados por los equipos médicos son diferentes por cada equipo. Otros autores consideran más características: en veracidad, la información generada y almacenada es necesario mantener limpia y precisa </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICICCS.2016.7542360&quot;,&quot;ISBN&quot;:&quot;978-1-5090-2084-3&quot;,&quot;abstract&quot;:&quot;For the last century, the healthcare services and delivery including financing in India has been shrouded by life insurance challenges and importantly, shares key landmarks with general insurance. 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Those datasets are producing in every second via a wide range of sources such as Facebook and Twitter. The big data has a basic three pillars- Volume (size), Variety (types) and Velocity (speed). Big data is a prediction platform where lots of dataset are available in the form of raw material, so we need an appropriate technique to process it and predict some valuable information for the present as well as future use. The sampling techniques are applicable to handle large datasets where all data sets are not possible to process at a time. In this situation, the sampling techniques play a very important role in the processing of big huge data. In this paper, we have proposed a parameter estimation model for big data to predict the mean size of large data by using the sampling technique. The proposed model can reduce the processing time as compare to the existing model because the sampling mechanism is to select small sample (n) from the entire datasets (N) and estimate the unknown parameters which are known as prediction results. Furthermore, we have presented the demonstration of the proposed algorithm on dynamic dataset and also compared result with simple random sampling (SRS) technique.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Alim&quot;,&quot;given&quot;:&quot;Abdul&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Shukla&quot;,&quot;given&quot;:&quot;Diwakar&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IDEA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2020&quot;]]},&quot;title&quot;:&quot;A parameter estimation model of big data setup&quot;,&quot;type&quot;:&quot;article-journal&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=21302b45-2345-488e-a333-4395ccb458d4&quot;,&quot;http://www.mendeley.com/documents/?uuid=119a424e-dbf6-4019-b123-f0d2b0245b2a&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[3]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[3]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[3]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[3]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><a name="_Toc84111612"><b style='mso-bidi-font-weight:normal'><span lang=ES>3.2 Casos del uso de Big Data en Seguros de Salud</span></b></a><b style='mso-bidi-font-weight:normal'><span lang=ES>.- </span></b><span lang=ES>Existen problemas de ética, uso, seguridad y privacidad para implementar Big Data en datos de salud y para alcanzar un buen éxito es necesario garantizar la seguridad y privacidad en los datos médicos, además de adoptar estándares y establecer un buen diseño del procesamiento de datos </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/FTC.2016.7821747&quot;,&quot;ISBN&quot;:&quot;978-1-5090-4171-8&quot;,&quot;abstract&quot;:&quot;The healthcare system consists of large volumes of data which are usually generated from diverse sources such as physicians' case notes, hospital admission notes, discharge summaries, pharmacies, insurance companies, medical imaging, laboratories, sensor based devices, genomics, social media as well as articles in medical journals. Healthcare data are however very complex and difficult to manage. This is as a result of the astronomical growth of healthcare data, the high speed at which these data are generated as well as the diversity of data types in healthcare. The capturing, storage, analysis and retrieval of health related data are rapidly shifting from paper based system towards digitization. However, the vast volume as well as the complexity of these data makes it difficult for the data to be processed and analyzed by traditional approaches and techniques. Consequently, technologies such as cloud computing and virtualization are now gradually used for processing massive data effectively and securely in healthcare. Hence, the healthcare system is swiftly becoming a big data industry. Thus, this paper examines the concept of big data in healthcare, its benefits and attendant challenges. This paper revealed that the fragmentation of healthcare data, ethical issues, usability issues as well as security and privacy issues are some of the factors impeding the successful implementation of big data in healthcare. This paper therefore suggests that ensuring security and privacy of healthcare data, the adoption of a standardized healthcare terminology, education strategy and the design of usable systems for processing large volumes of data are some of the ways of successfully implementing big data in healthcare.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Olaronke&quot;,&quot;given&quot;:&quot;Iroju&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Oluwaseun&quot;,&quot;given&quot;:&quot;Ojerinde&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;FTC&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;December&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;,&quot;12&quot;]]},&quot;page&quot;:&quot;1152-1157&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Big data in healthcare&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=f9a8e971-ce12-48e7-949c-68c14b07f6be&quot;,&quot;http://www.mendeley.com/documents/?uuid=cade217b-1b4b-4f36-9acb-f0050293fb8c&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[1]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[1]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[1]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[1]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. En India el gobierno respalda fuertemente la atención médica y los investigadores aplican BD sobre datos de salud, con ello obtienen conocimiento sobre los cuidados de salud que deben tener en los pacientes </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICICCS.2016.7542360&quot;,&quot;ISBN&quot;:&quot;978-1-5090-2084-3&quot;,&quot;abstract&quot;:&quot;For the last century, the healthcare services and delivery including financing in India has been shrouded by life insurance challenges and importantly, shares key landmarks with general insurance. Many experts, practitioners and policy makers including researchers identified that, there is a big gap in the knowledge about innovations in public and private health financing and delivery. Apparent entails the necessity to alter the prevailing structure of the present health care services by applying Big Data Analytics. In this paper we'll discuss the various assorted implications, characteristics beside decrease in healthcare costs and how this new era of advanced and improved Data management is helping in Insurance Sector, more even we have to pay specific attention to the use cases which leads and drive new technology and eventually lead towards economic progress.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Gupta&quot;,&quot;given&quot;:&quot;Shalu&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Tripathi&quot;,&quot;given&quot;:&quot;Pooja&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICICCS&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;Iciccs&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;,&quot;2&quot;]]},&quot;page&quot;:&quot;64-69&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;An emerging trend of big data analytics with health insurance in India&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=700f2858-c030-4ca0-afdf-319814b0fa7e&quot;,&quot;http://www.mendeley.com/documents/?uuid=b272e54d-383c-4792-9388-be7c3377813f&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[5]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[5]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[5]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[5]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. En Japón para identificar las causas de reclamos en los seguros, su modelo propone una revisión de medicación en lapsos de tiempo, estas prescripciones se descomponen para visualizar el comportamiento de medicina recomendada por el médico </span><!--[if supportFields]><span lang=ES><span style='mso-element: field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICDE.2019.00209&quot;,&quot;ISBN&quot;:&quot;978-1-5386-7474-1&quot;,&quot;ISSN&quot;:&quot;10844627&quot;,&quot;abstract&quot;:&quot;Understanding the spread of diseases and the use of medicines is of practical importance for various organizations, such as medical providers, medical payers, and national governments. This study aims to detect the change in the prescription trends and to identify its cause through an analysis of Medical Insurance Claims (MICs), which comprise the specifications of medical fees charged to health insurers. Our approach is two-fold. (1) We propose a latent variable model that simulates the medication behavior of physicians to accurately reproduce monthly prescription time series from the MIC data, where prescription links between the diseases and medicines are missing. (2) We apply a state space model with intervention variables to decompose the monthly prescription time series into different components including seasonality and structural changes. Using a large dataset consisting of 3.5-year MIC records, we conduct experiments to evaluate our approach in terms of accuracy, usefulness, and efficiency. We also demonstrate three applications for our medical analysis.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Umemoto&quot;,&quot;given&quot;:&quot;Kazutoshi&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Goda&quot;,&quot;given&quot;:&quot;Kazuo&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICDE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2019&quot;,&quot;4&quot;]]},&quot;page&quot;:&quot;1928-1939&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;A Prescription Trend Analysis using Medical Insurance Claim Big Data&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;2019-April&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=516175e2-ee5d-4511-be25-ac7a6fb36be1&quot;,&quot;http://www.mendeley.com/documents/?uuid=ef9430fc-799c-44e2-a2e4-3e111577d5ce&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[7]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[7]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[7]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[7]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. El seguro de salud nacional en Indonesia tuvo un aumento en las tarifas del año 2019, por esta razón aplicaron Big Data para capturar y estudiar los sentimientos expresados por los usuarios en una red social </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICoSTA48221.2020.1570615599&quot;,&quot;ISBN&quot;:&quot;9781728130835&quot;,&quot;abstract&quot;:&quot;Mobile phones are devices that always embedded in the society of Indonesian people, around 91% of Indonesians population use mobile phones and around 60% are smartphones. The most widely used smartphone application is social media, around 60% of Indonesia's population uses social media. The number of social media users in Indonesia opens up opportunities to analyze these social media data. Social media data mining, commonly referred to as text mining, can help stakeholders implement a data driven policy. In this paper will discuss the public perception on social media related to the issue of rising fees national health insurance or Badan Penyelenggara Jaminan Sosial (BPJS Kesehatan). The social media analyzed is Twitter because it can provide a comprehensive sentiment and discussion of an issue. We used Drone Emprit system to analyze social media data, Drone Emprit is a big data system that captures and analyzes discussion and sentiments on social media platforms, in this case Twitter. The data analyzed are all conversations that took place over 60 days in the time span of 22 September to 22 November 2019. During that time period 360,820 total conversations occurred with a percentage of sentiment of 91% showing negative sentiment, 5% positive and 4% neutral. This data shows that the majority of Indonesian people do not agree with the increase in fees of national health insurances (BPJS Kesehatan).&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Laagu&quot;,&quot;given&quot;:&quot;Muh Asnoer&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Setyo Arifin&quot;,&quot;given&quot;:&quot;Ajib&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICoSTA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2020&quot;]]},&quot;title&quot;:&quot;Analysis the Issue of Increasing National Health Insurance Rates&quot;,&quot;type&quot;:&quot;article-journal&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=44c8b715-1ad2-4442-a6e0-93690d930990&quot;,&quot;http://www.mendeley.com/documents/?uuid=f88023ce-3d39-4476-a4c8-80d48da39735&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[14]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[14]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[14]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[14]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. La reducción de fraudes en las compañías de seguros de salud fue minimizada a través de un framework que revisa los reclamos de seguros con la identificación de datos en los documentos </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICBDAA.2017.8284114&quot;,&quot;ISBN&quot;:&quot;978-1-5386-0790-9&quot;,&quot;abstract&quot;:&quot;Deliberate cheating by concealing and omitting facts while claiming from health insurance providers is considered as one of fraudulent activities in the health insurance domain which has led to significant amount of monetary loss to the providers. In view of the above, careful scanning of the submitted claim documents need to be conducted by the insurance companies in order to spot any discrepancy that indicates fraud. For this purpose, manual detection is neither easy nor practical as the claim documents received are plentiful and for diverse medical treatments. Hence, this paper shares the initial stage of our study which is aimed to propose an approach for detecting fraudulent health insurance claims by identifying correlation or association between some of the attributes on the claim documents. With the application of a data mining technique of association rules, this study advocates that the successful determination of correlated attributes can adequately address the discrepancies of data in fraudulent claims and thus reduce fraud in health insurance.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Kareem&quot;,&quot;given&quot;:&quot;Saba&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Binti Ahmad&quot;,&quot;given&quot;:&quot;Rohiza&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICBDA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2017&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;99-104&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Framework for the identification of fraudulent health insurance claims&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;2018-Janua&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=ffe5af9a-8128-4960-acdc-97af8c3f7383&quot;,&quot;http://www.mendeley.com/documents/?uuid=2fb1561c-82a1-4901-b617-bba095bc87cd&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[11]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[11]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[11]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[11]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. Una comisión del gobierno de Australia analizó los gastos anuales de hospitales en 3 mil millones de dólares australianos, para reducir costos y aumentar la calidad de vida </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/JBHI.2015.2402692&quot;,&quot;ISSN&quot;:&quot;2168-2194&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Xie&quot;,&quot;given&quot;:&quot;Yang&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Schreier&quot;,&quot;given&quot;:&quot;Gunter&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Chang&quot;,&quot;given&quot;:&quot;David C W&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Neubauer&quot;,&quot;given&quot;:&quot;Sandra&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Liu&quot;,&quot;given&quot;:&quot;Ying&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Redmond&quot;,&quot;given&quot;:&quot;Stephen J&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Lovell&quot;,&quot;given&quot;:&quot;Nigel H&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IEEE Journal of Biomedical and Health Informatics&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;4&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2015&quot;,&quot;7&quot;]]},&quot;page&quot;:&quot;1224-1233&quot;,&quot;title&quot;:&quot;Predicting Days in Hospital Using Health Insurance Claims&quot;,&quot;type&quot;:&quot;article-journal&quot;,&quot;volume&quot;:&quot;19&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=ae489ff9-9081-4727-b5c0-5e1dc8d878d1&quot;,&quot;http://www.mendeley.com/documents/?uuid=9a09c1c7-d7e3-4666-a7f7-9fc4bea92c83&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[30]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[30]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[30]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[30]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. Existen algoritmos que trabajan sobre Big Data de compañías en seguros de salud, estos algoritmos evitan o localizan los posibles fraudes en los reclamos de una provincia de China </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/BigDataSecurityHPSCIDS52275.2021.00021&quot;,&quot;ISBN&quot;:&quot;978-1-6654-3927-5&quot;,&quot;abstract&quot;:&quot;After years of development, the traditional decision tree algorithm implementations represented by LightGBM has been very mature and widely used in various classification problems. However, when applying LightGBM to detecting fraud in health insurance data, we find that the performance of LightGBM is not ideal due to the large imbalance between the number of fraud examples and normal examples. To solve this problem, we propose a simple and effective LightGBM-based hard example mining algorithm (LHEM) for detecting health insurance fraud. Our motivation is to detect the large number of simple examples and the small number of hard examples in the dataset. Selecting these hard examples and discarding simple examples can balance the ratio of fraud and normal examples, thus improving the performance of the original model. We use the health insurance data collected in Zhejiang Jinhua to test our new method, and prove that the performance of our new method is better than LightGBM.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Yang&quot;,&quot;given&quot;:&quot;Wenyi&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Hu&quot;,&quot;given&quot;:&quot;Wenhui&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IEEE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;,&quot;5&quot;]]},&quot;page&quot;:&quot;57-62&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Research on Algorithm for Health Insurance Data Fraud Detection&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=d423330a-f0f8-42dc-bddb-e400c9038d8f&quot;,&quot;http://www.mendeley.com/documents/?uuid=b3dcc159-c6d8-40bd-89a0-579bfff76177&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[31]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[31]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[31]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[31]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. En Australia se utilizan dos sistemas computacionales que analizan los reclamos en seguros de salud y utiliza los macro datos para descubrir fraudes, derroches o sobre precios en las transacciones </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/MITP.2013.55&quot;,&quot;ISSN&quot;:&quot;1520-9202&quot;,&quot;abstract&quot;:&quot;The healthcare sector deals with large volumes of electronic data related to patient services. This article describes two novel applications that leverage big data to detect fraud, abuse, waste, and errors in health insurance claims, thus reducing recurrent losses and facilitating enhanced patient care. The results indicate that claim anomalies detected using these applications help private health insurance funds recover hidden cost overruns that aren't detectable using transaction processing systems. This article is part of a special issue on leveraging big data and business analytics. © 1999-2012 IEEE.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Srinivasan&quot;,&quot;given&quot;:&quot;Uma&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Arunasalam&quot;,&quot;given&quot;:&quot;Bavani&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IT Professional&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;6&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2013&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;21-28&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Leveraging Big Data Analytics to Reduce Healthcare Costs&quot;,&quot;type&quot;:&quot;article-journal&quot;,&quot;volume&quot;:&quot;15&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=d9f2a937-2cf1-4031-973d-7eb4d64e10e5&quot;,&quot;http://www.mendeley.com/documents/?uuid=fb7eaa51-522d-4c16-a146-0a81e95219dd&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[32]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[32]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[32]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[32]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>, en este caso particular los investigadores aplicaron un modelo cuantitativo de Big Data en seguros de salud con datos de 1500 reclamos </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICSCSE.2016.0159&quot;,&quot;ISBN&quot;:&quot;978-1-5090-5530-2&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Jiang&quot;,&quot;given&quot;:&quot;Lesheng&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICSCSE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;590-593&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Quantitative Model of Insurance Risk Management System Based on Big Data&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=6d7c2958-6cd1-4af7-8795-6ecd401f892d&quot;,&quot;http://www.mendeley.com/documents/?uuid=24ea7482-0db2-43aa-a2ca-09ddb113d8b0&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[33]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[33]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[33]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[33]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>. Se presenta un enfoque que se aplica a macro datos generados por el sistema informático en seguros de medicina para encontrar errores de datos o datos falsificados </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/SUMMA48161.2019.8947605&quot;,&quot;ISBN&quot;:&quot;978-1-7281-4911-0&quot;,&quot;abstract&quot;:&quot;Big data characterizing the quality of medical health care services provided to the population contain anomaly observations, which are either results of technical errors in filling databases, or falsified data. In the context of insurance medicine, the actual task is to identify such observation. The paper presents an approach to identify outliers in the database of medical health care information system based on the combination of Isolating Forest algorithm and subsequent neural network classifier.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Sysoev&quot;,&quot;given&quot;:&quot;Anton&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Scheglevatych&quot;,&quot;given&quot;:&quot;Roman&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;SUMMA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2019&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;359-363&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Combined Approach to Detect Anomalies in Health Care Datasets&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=96b994ae-efa6-4157-a869-7dc0dfaee3cf&quot;,&quot;http://www.mendeley.com/documents/?uuid=cbb8c16a-2271-465a-a243-004c394ceae7&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[34]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[34]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[34]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[34]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><a name="_Toc84111613"><b style='mso-bidi-font-weight:normal'><span lang=ES>3.3 Datos generales en las compañías de seguros de </span></b></a><span class=GramE><span style='mso-bookmark:_Toc84111613'><b style='mso-bidi-font-weight: normal'><span lang=ES>salud</span></b></span><b style='mso-bidi-font-weight: normal'><span lang=ES>.-</span></b></span><b style='mso-bidi-font-weight:normal'><span lang=ES> </span></b><span lang=ES>Se explica algunos datos en las compañías en seguros de salud, estos datos contienen a nivel general, una solicitud de seguro individual, declaración del médico, formulario de reclamos, anexos de explicaciones médicas, anexo de datos de los dependientes, formulario de pago, anexo de cambios o actualización, entre los más relevantes. En hojas electrónicas están cotizaciones, calculadoras de constitución física, calculadoras de prorrateos e inclusiones. En procesadores de palabras están las cartas avales que se envían a los proveedores para que den cobertura directa a los clientes, las cartas de asegurabilidad que detallan la información general de la cobertura que tienen los clientes, las cartas de siniestralidad que contiene resumen de los reclamos que han presentado los clientes, el certificado de cobertura, aviso de prima, recibos de pago, tarjetas de membresía y documentos de anexos. En la página web de la empresa los visitantes tienen la disponibilidad de dejar mensajes.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><b style='mso-bidi-font-weight:normal'><span lang=ES>4. <span class=GramE>Resultados.-</span> </span></b><span lang=ES>En esta fase se obtuvieron tres resultados de acuerdo con los objetivos específicos del estudio de investigación.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;margin-right:0in;margin-bottom: 12.0pt;margin-left:0in;text-align:justify'><b style='mso-bidi-font-weight:normal'><span lang=ES>4.1. Categorizar diferentes arquitecturas computacionales para seguros de la salud mediante una revisión de literatura relevante.- </span></b><span lang=ES>Se ha categorizado las arquitecturas de acuerdo a los objetivos encontrados en otros trabajos relevantes, y las categorías son: prevención de enfermedades </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICICCS.2016.7542360&quot;,&quot;ISBN&quot;:&quot;978-1-5090-2084-3&quot;,&quot;abstract&quot;:&quot;For the last century, the healthcare services and delivery including financing in India has been shrouded by life insurance challenges and importantly, shares key landmarks with general insurance. Many experts, practitioners and policy makers including researchers identified that, there is a big gap in the knowledge about innovations in public and private health financing and delivery. Apparent entails the necessity to alter the prevailing structure of the present health care services by applying Big Data Analytics. In this paper we'll discuss the various assorted implications, characteristics beside decrease in healthcare costs and how this new era of advanced and improved Data management is helping in Insurance Sector, more even we have to pay specific attention to the use cases which leads and drive new technology and eventually lead towards economic progress.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Gupta&quot;,&quot;given&quot;:&quot;Shalu&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Tripathi&quot;,&quot;given&quot;:&quot;Pooja&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICICCS&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;Iciccs&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;,&quot;2&quot;]]},&quot;page&quot;:&quot;64-69&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;An emerging trend of big data analytics with health insurance in India&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=700f2858-c030-4ca0-afdf-319814b0fa7e&quot;,&quot;http://www.mendeley.com/documents/?uuid=b272e54d-383c-4792-9388-be7c3377813f&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[5]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[5]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[5]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[5]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>, </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICSCSE.2016.0159&quot;,&quot;ISBN&quot;:&quot;978-1-5090-5530-2&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Jiang&quot;,&quot;given&quot;:&quot;Lesheng&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICSCSE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;590-593&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Quantitative Model of Insurance Risk Management System Based on Big Data&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=24ea7482-0db2-43aa-a2ca-09ddb113d8b0&quot;,&quot;http://www.mendeley.com/documents/?uuid=6d7c2958-6cd1-4af7-8795-6ecd401f892d&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[33]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[33]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[33]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[33]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>, </span><!--[if supportFields]><span lang=ES><span style='mso-element: field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/3368756.3369103&quot;,&quot;ISBN&quot;:&quot;9781450362894&quot;,&quot;abstract&quot;:&quot;Clustering techniques aim to discover groupings of a set of patterns or data and are widely used in any discipline that involves analysis of multivariate data. Their applications in different fields are multiple and diverse. They can also be used in health insurance, which is an important part of healthcare. The use of clustering techniques in this field will provide opportunities and solutions for decision makers in order to monitor the insurance coverage in general and health insurance in particular. The aim of this paper is to create clusters of the insured population by means of an unsupervised machine learning algorithm, which is a partitional clustering technique that has proved to be efficient and fast, which is K-means. We present and discuss the results obtained by using this clustering method.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Zahi&quot;,&quot;given&quot;:&quot;Sara&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Achchab&quot;,&quot;given&quot;:&quot;Boujemâa&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICSCA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2019&quot;,&quot;10&quot;,&quot;2&quot;]]},&quot;page&quot;:&quot;1-6&quot;,&quot;publisher&quot;:&quot;ACM&quot;,&quot;publisher-place&quot;:&quot;New York, NY, USA&quot;,&quot;title&quot;:&quot;Clustering of the population benefiting from health insurance&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=c95d074b-acd6-42a7-870b-487a6afb316e&quot;,&quot;http://www.mendeley.com/documents/?uuid=fa51bfbf-a5d6-4976-ba6a-4fb3c0b785d8&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[35]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[35]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[35]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[35]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>, </span><!--[if supportFields]><span lang=ES><span style='mso-element: field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/CHASE.2017.81&quot;,&quot;ISBN&quot;:&quot;978-1-5090-4722-2&quot;,&quot;abstract&quot;:&quot;Public health researchers increasingly recognize that to advance their field they must grapple with the availability of increasingly large (i.e., thousands of variables) traditional population-level datasets (e.g., electronic medical records), while at the same time integrating additional large datasets (e.g., data on genomics, the microbiome, environmental exposures, socioeconomic factors, and health behaviors). Leveraging these multiple forms of data might well provide unique and unexpected discoveries about the determinants of health and wellbeing. However, we are in the very early stages of advancing the techniques required to understand and analyze big population-level data for public health research. To address this problem, this paper describes how we propose that big data can be efficiently used for public health discoveries. We show that data analytics techniques traditionally employed in public health studies are not up to the task of the data we now have in hand. Instead we present techniques adapted from big data visualization and analytics approaches used in other domains that can be used to answer important public health questions utilizing these existing and new datasets. Our findings are based on an exploratory big data case study carried out in San Diego County, California where we analyzed thousands of variables related to health to gain interesting insights on the determinants of several health outcomes, including life expectancy and anxiety disorders. These findings provide a promising early indication that public health research will benefit from the larger set of activities in contemporary big data research.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Katsis&quot;,&quot;given&quot;:&quot;Yannis&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Balac&quot;,&quot;given&quot;:&quot;Natasha&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IEEE/ACM&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2017&quot;,&quot;7&quot;]]},&quot;page&quot;:&quot;222-231&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Big Data Techniques for Public Health&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=73aef023-3d17-4b7d-8f19-a3634b18c827&quot;,&quot;http://www.mendeley.com/documents/?uuid=fd1bdeb5-9fc2-4f84-9459-12f4de72d3ea&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[36]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[36]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[36]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[36]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES> y </span><!--[if supportFields]><span lang=ES><span style='mso-element: field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/3348400.3348414&quot;,&quot;ISBN&quot;:&quot;9781450371674&quot;,&quot;abstract&quot;:&quot;Big Data is the biggest emerging trend and promise in today's technology-driven world. It is continuing to create a lot of buzz in not only the field of technology, but across the world. It promises substantial involvements, vast changes, modernizations, and integration with and within people's ongoing life. It makes the world more demanding and helps with making prompt and appropriate decisions in real time. This paper aims to provide a comprehensive analysis of the health industry and health care system in Australia that are relevant to the consequences formed by Big Data. This paper primarily uses a secondary research analysis method to provide a wide-ranging investigation into the positive and negative consequences of health issues relevant to Big Data, the architects of those consequences, and those overstated by the consequences. The secondary resources are subject to journal articles, reports, conference proceedings, media articles, corporation-based documents, blogs and other appropriate information. In the future, the investigation will continue by employing Mixed Methodology (Qualitative and Quantitative) in relation to Big Data usage in the Australian Health industry. The paper initially finds that Big Data is an evidence source in health care and provides useful insight into the Australian healthcare system. It is steadily reducing the cost of the Australian healthcare system and improving patients' outcomes in Australia. Big data can not only improve the affairs between public and health enterprises, but can also make life better by increasing efficiency and modernization.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Karim&quot;,&quot;given&quot;:&quot;Shakir&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Gide&quot;,&quot;given&quot;:&quot;Ergun&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICMSTTL&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2019&quot;]]},&quot;page&quot;:&quot;34-38&quot;,&quot;publisher&quot;:&quot;ACM Press&quot;,&quot;publisher-place&quot;:&quot;New York, New York, USA&quot;,&quot;title&quot;:&quot;The Impact of Big Data on Health Care Services in Australia&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=2eb4af3a-baf7-490a-85c8-ae35cec18386&quot;,&quot;http://www.mendeley.com/documents/?uuid=c37c0737-c685-43ea-bfbf-52fb2be7b7f7&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[37]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[37]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[37]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[37]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES> es decir, el 25% se concentraron en obtener mejores prácticas de salud o alimentación; causas de reclamos </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICDE.2019.00209&quot;,&quot;ISBN&quot;:&quot;978-1-5386-7474-1&quot;,&quot;ISSN&quot;:&quot;10844627&quot;,&quot;abstract&quot;:&quot;Understanding the spread of diseases and the use of medicines is of practical importance for various organizations, such as medical providers, medical payers, and national governments. This study aims to detect the change in the prescription trends and to identify its cause through an analysis of Medical Insurance Claims (MICs), which comprise the specifications of medical fees charged to health insurers. Our approach is two-fold. (1) We propose a latent variable model that simulates the medication behavior of physicians to accurately reproduce monthly prescription time series from the MIC data, where prescription links between the diseases and medicines are missing. (2) We apply a state space model with intervention variables to decompose the monthly prescription time series into different components including seasonality and structural changes. Using a large dataset consisting of 3.5-year MIC records, we conduct experiments to evaluate our approach in terms of accuracy, usefulness, and efficiency. We also demonstrate three applications for our medical analysis.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Umemoto&quot;,&quot;given&quot;:&quot;Kazutoshi&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Goda&quot;,&quot;given&quot;:&quot;Kazuo&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICDE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2019&quot;,&quot;4&quot;]]},&quot;page&quot;:&quot;1928-1939&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;A Prescription Trend Analysis using Medical Insurance Claim Big Data&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;2019-April&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=516175e2-ee5d-4511-be25-ac7a6fb36be1&quot;,&quot;http://www.mendeley.com/documents/?uuid=ef9430fc-799c-44e2-a2e4-3e111577d5ce&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[7]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[7]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[7]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[7]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES> y </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/2684103.2684178&quot;,&quot;ISBN&quot;:&quot;9781450330084&quot;,&quot;abstract&quot;:&quot;The current National health insurance in Taiwan is a system widely praised by other countries, but the rising healthcare expenditure has been a serious financial issue in recent years. Many scholars have adopted the National Health Insurance Research Database (NHIRD) for all sorts of research studies. However, few have conducted research from the perspective of multiple-level stakeholders, which remains to be a worth exploring research direction. This research addressed the above issue by constructing a Hierarchical linear modeling (HLM) based on the available data in NHIRD in order to identify key factors influencing health spending. A future work is to conduct an empirical study to validate the HLM model, which can be also good reference for future insightful research on multiple-stakeholder perspective.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Chen&quot;,&quot;given&quot;:&quot;Ying-Kai&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Tao&quot;,&quot;given&quot;:&quot;Yu-Hui&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICAMCM&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2014&quot;,&quot;12&quot;,&quot;8&quot;]]},&quot;page&quot;:&quot;353-355&quot;,&quot;publisher&quot;:&quot;ACM&quot;,&quot;publisher-place&quot;:&quot;New York, NY, USA&quot;,&quot;title&quot;:&quot;Analyzing the Healthcare Expenditure of National Health Insurance&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=5f7c7fbd-383b-4ffd-81fa-119813c196f7&quot;,&quot;http://www.mendeley.com/documents/?uuid=6c815675-5dc4-4fad-8a0b-fbb27993f3c2&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[38]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[38]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[38]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[38]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES> es decir, el 10% se concentraron en las principales causas o razones que hacen los clientes en sus reclamos; expresión de sentimientos </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICoSTA48221.2020.1570615599&quot;,&quot;ISBN&quot;:&quot;9781728130835&quot;,&quot;abstract&quot;:&quot;Mobile phones are devices that always embedded in the society of Indonesian people, around 91% of Indonesians population use mobile phones and around 60% are smartphones. The most widely used smartphone application is social media, around 60% of Indonesia's population uses social media. The number of social media users in Indonesia opens up opportunities to analyze these social media data. Social media data mining, commonly referred to as text mining, can help stakeholders implement a data driven policy. In this paper will discuss the public perception on social media related to the issue of rising fees national health insurance or Badan Penyelenggara Jaminan Sosial (BPJS Kesehatan). The social media analyzed is Twitter because it can provide a comprehensive sentiment and discussion of an issue. We used Drone Emprit system to analyze social media data, Drone Emprit is a big data system that captures and analyzes discussion and sentiments on social media platforms, in this case Twitter. The data analyzed are all conversations that took place over 60 days in the time span of 22 September to 22 November 2019. During that time period 360,820 total conversations occurred with a percentage of sentiment of 91% showing negative sentiment, 5% positive and 4% neutral. This data shows that the majority of Indonesian people do not agree with the increase in fees of national health insurances (BPJS Kesehatan).&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Laagu&quot;,&quot;given&quot;:&quot;Muh Asnoer&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Setyo Arifin&quot;,&quot;given&quot;:&quot;Ajib&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICoSTA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2020&quot;]]},&quot;title&quot;:&quot;Analysis the Issue of Increasing National Health Insurance Rates&quot;,&quot;type&quot;:&quot;article-journal&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=44c8b715-1ad2-4442-a6e0-93690d930990&quot;,&quot;http://www.mendeley.com/documents/?uuid=f88023ce-3d39-4476-a4c8-80d48da39735&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[14]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[14]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[14]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[14]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES> es decir, el 5% se concentró en conocer las opiniones positivas o negativas sobre los reclamos; fraudes en reclamos </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICBDAA.2017.8284114&quot;,&quot;ISBN&quot;:&quot;978-1-5386-0790-9&quot;,&quot;abstract&quot;:&quot;Deliberate cheating by concealing and omitting facts while claiming from health insurance providers is considered as one of fraudulent activities in the health insurance domain which has led to significant amount of monetary loss to the providers. In view of the above, careful scanning of the submitted claim documents need to be conducted by the insurance companies in order to spot any discrepancy that indicates fraud. For this purpose, manual detection is neither easy nor practical as the claim documents received are plentiful and for diverse medical treatments. Hence, this paper shares the initial stage of our study which is aimed to propose an approach for detecting fraudulent health insurance claims by identifying correlation or association between some of the attributes on the claim documents. With the application of a data mining technique of association rules, this study advocates that the successful determination of correlated attributes can adequately address the discrepancies of data in fraudulent claims and thus reduce fraud in health insurance.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Kareem&quot;,&quot;given&quot;:&quot;Saba&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Binti Ahmad&quot;,&quot;given&quot;:&quot;Rohiza&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICBDA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2017&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;99-104&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Framework for the identification of fraudulent health insurance claims&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;2018-Janua&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=ffe5af9a-8128-4960-acdc-97af8c3f7383&quot;,&quot;http://www.mendeley.com/documents/?uuid=2fb1561c-82a1-4901-b617-bba095bc87cd&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[11]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[11]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[11]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[11]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>, </span><!--[if supportFields]><span lang=ES><span style='mso-element: field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/BigDataSecurityHPSCIDS52275.2021.00021&quot;,&quot;ISBN&quot;:&quot;978-1-6654-3927-5&quot;,&quot;abstract&quot;:&quot;After years of development, the traditional decision tree algorithm implementations represented by LightGBM has been very mature and widely used in various classification problems. However, when applying LightGBM to detecting fraud in health insurance data, we find that the performance of LightGBM is not ideal due to the large imbalance between the number of fraud examples and normal examples. To solve this problem, we propose a simple and effective LightGBM-based hard example mining algorithm (LHEM) for detecting health insurance fraud. Our motivation is to detect the large number of simple examples and the small number of hard examples in the dataset. Selecting these hard examples and discarding simple examples can balance the ratio of fraud and normal examples, thus improving the performance of the original model. We use the health insurance data collected in Zhejiang Jinhua to test our new method, and prove that the performance of our new method is better than LightGBM.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Yang&quot;,&quot;given&quot;:&quot;Wenyi&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Hu&quot;,&quot;given&quot;:&quot;Wenhui&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IEEE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;,&quot;5&quot;]]},&quot;page&quot;:&quot;57-62&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Research on Algorithm for Health Insurance Data Fraud Detection&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=b3dcc159-c6d8-40bd-89a0-579bfff76177&quot;,&quot;http://www.mendeley.com/documents/?uuid=d423330a-f0f8-42dc-bddb-e400c9038d8f&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[31]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[31]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[31]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[31]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>, </span><!--[if supportFields]><span lang=ES><span style='mso-element: field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/MITP.2013.55&quot;,&quot;ISSN&quot;:&quot;1520-9202&quot;,&quot;abstract&quot;:&quot;The healthcare sector deals with large volumes of electronic data related to patient services. This article describes two novel applications that leverage big data to detect fraud, abuse, waste, and errors in health insurance claims, thus reducing recurrent losses and facilitating enhanced patient care. The results indicate that claim anomalies detected using these applications help private health insurance funds recover hidden cost overruns that aren't detectable using transaction processing systems. This article is part of a special issue on leveraging big data and business analytics. © 1999-2012 IEEE.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Srinivasan&quot;,&quot;given&quot;:&quot;Uma&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Arunasalam&quot;,&quot;given&quot;:&quot;Bavani&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IT Professional&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;6&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2013&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;21-28&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Leveraging Big Data Analytics to Reduce Healthcare Costs&quot;,&quot;type&quot;:&quot;article-journal&quot;,&quot;volume&quot;:&quot;15&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=fb7eaa51-522d-4c16-a146-0a81e95219dd&quot;,&quot;http://www.mendeley.com/documents/?uuid=d9f2a937-2cf1-4031-973d-7eb4d64e10e5&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[32]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[32]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[32]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[32]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES>, </span><!--[if supportFields]><span lang=ES><span style='mso-element: field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/SUMMA48161.2019.8947605&quot;,&quot;ISBN&quot;:&quot;978-1-7281-4911-0&quot;,&quot;abstract&quot;:&quot;Big data characterizing the quality of medical health care services provided to the population contain anomaly observations, which are either results of technical errors in filling databases, or falsified data. In the context of insurance medicine, the actual task is to identify such observation. 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A major cause of this increase are payment errors made by the insurance companies while processing claims. These errors often result in extra administrative effort to re-process (or rework) the claim which accounts for up to 30% of the administrative staff in a typical health insurer. We describe a system that helps reduce these errors using machine learning techniques by predicting claims that will need to be reworked, generating explanations to help the auditors correct these claims, and experiment with feature selection, concept drift, and active learning to collect feedback from the auditors to improve over time. We describe our framework, problem formulation, evaluation metrics, and experimental results on claims data from a large US health insurer. We show that our system results in an order of magnitude better precision (hit rate) over existing approaches which is accurate enough to potentially result in over $15-25 million in savings for a typical insurer. 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This quantitative approach contributes to the literature by providing better estimates of causality, from more perspectives, thus delivering more credible conclusions. We find that reducing social insurance contributions can affect firm insurance behavior through compliance. On the one hand, lowering the contribution rate has led to an increase in compliance. 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In this paper, we will analyze the scope of the problem, and present current status and challenges in this area. Secondly we will propose a prototype schema to address this problem through Internet of things and real-time big data predictive analytics. Finally we will discuss some technical and non-technical challenges observed.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Deng&quot;,&quot;given&quot;:&quot;Xin&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Wu&quot;,&quot;given&quot;:&quot;Donghui&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ACM&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2015&quot;,&quot;9&quot;,&quot;9&quot;]]},&quot;page&quot;:&quot;674-674&quot;,&quot;publisher&quot;:&quot;ACM&quot;,&quot;publisher-place&quot;:&quot;New York, NY, USA&quot;,&quot;title&quot;:&quot;Senior health management through internet of things and real-time big data analytics&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=b14e2bde-3d96-4a83-9ff6-9dc81ee60cb0&quot;,&quot;http://www.mendeley.com/documents/?uuid=2afd6b6e-6750-4516-8718-47589e3a7ba7&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[43]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[43]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[43]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[43]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES> es decir, 10% se concentra en conocer qué enfermedades podrían acaecer sobre la población; seguridad de datos </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/3090354.3090388&quot;,&quot;ISBN&quot;:&quot;9781450348522&quot;,&quot;abstract&quot;:&quot;In recent year, cloud computing and big data have become the hottest research topics which attract the attention of researchers due to its potential to provide major benefits to the industry and the community. Currently, the Organizations have a new option to outsource their massive data in the cloud without having to worry about the size of data or the capacity of memory. However, moving confidential and sensitive data from trusted domain of the data owners to public cloud will cause various security and privacy risks. Furthermore, the increasing amount of big data outsourced in the cloud increases the chance of breaching the privacy and security of these data. Despite all the research that has been done in this area, big data storage security and privacy remains one of the major concerns of organizations that adopt the cloud computing and big data technologies. Thus to ensure better data security we need for focus on two major problems which are the access control and encryption policies. In this paper, we propose a new hybrid model that enhances the security and privacy of big data shared in cloud environment using an access control scheme based on KP-ABE and authentication system. Our approach provides a flexible fine-grained access control of big data stored and shared in the cloud computing such that the encrypted data can only be accessed by authorized users.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Sara&quot;,&quot;given&quot;:&quot;Amghar&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Yassine&quot;,&quot;given&quot;:&quot;Tabaa&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;iCBDCA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2017&quot;,&quot;3&quot;,&quot;29&quot;]]},&quot;page&quot;:&quot;1-4&quot;,&quot;publisher&quot;:&quot;ACM&quot;,&quot;publisher-place&quot;:&quot;New York, NY, USA&quot;,&quot;title&quot;:&quot;Secure confidential big data sharing in cloud computing&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;Part F1294&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=5d3758e4-cf88-4f60-ab6a-85374c606613&quot;,&quot;http://www.mendeley.com/documents/?uuid=e1053e92-3ffb-434b-834e-794ff36deb04&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[44]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[44]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[44]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[44]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES> y </span><!--[if supportFields]><span lang=ES><span style='mso-element: field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/1125451.1125484&quot;,&quot;ISBN&quot;:&quot;1595932984&quot;,&quot;abstract&quot;:&quot;This Experience Report describes the challenges of evaluating the usability of a Web-based collaborative health insurance benefits planning application. The application was created by researchers at the National Institutes of Health and the University of Michigan.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Kantner&quot;,&quot;given&quot;:&quot;Laurie&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Goold&quot;,&quot;given&quot;:&quot;Susan Dorr&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;CHI&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2006&quot;,&quot;4&quot;,&quot;21&quot;]]},&quot;page&quot;:&quot;141-146&quot;,&quot;publisher&quot;:&quot;ACM&quot;,&quot;publisher-place&quot;:&quot;New York, NY, USA&quot;,&quot;title&quot;:&quot;Web tool for health insurance design by small groups&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=5ac42344-9a78-4238-9a2e-7cb214f7e7c9&quot;,&quot;http://www.mendeley.com/documents/?uuid=177a7b5f-fb73-44b4-88cc-3c88a44ad022&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[45]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[45]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[45]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[45]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES> es decir, 10% trabaja sobre seguridad de información sobre salud de los clientes, ver figura II.</span></p> <div align=center> <table class=MsoTableGrid border=0 cellspacing=0 cellpadding=0 style='border-collapse:collapse;mso-table-layout-alt:fixed;border:none; mso-yfti-tbllook:1184;mso-padding-alt:0in 5.4pt 0in 5.4pt;mso-border-insideh: none;mso-border-insidev:none'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;height:82.2pt'> <td width=520 valign=top style='width:389.85pt;padding:0in 5.4pt 0in 5.4pt; height:82.2pt'> <p class=TableParagraph align=center style='margin-top:.05pt;text-align:center'><span lang=ES-EC style='font-size:5.5pt;mso-bidi-font-size:11.0pt;mso-ansi-language: ES-EC;mso-fareast-language:ES-EC;mso-bidi-language:AR-SA;mso-no-proof:yes'><!--[if gte vml 1]><v:shape id="Imagen_x0020_1" o:spid="_x0000_i1028" type="#_x0000_t75" style='width:389pt; height:154pt;visibility:visible;mso-wrap-style:square'> <v:imagedata src="02_MIIUM23-03_Ecuador_Format.fld/image002.png" o:title=""/> </v:shape><![endif]--><![if !vml]><img width=389 height=154 src="http://www4.um.edu.uy/mailings/Imagenes/OJS_ING/Llerena008.jpg" v:shapes="Imagen_x0020_1"><![endif]></span><span lang=ES style='font-size:5.5pt;mso-bidi-font-size:11.0pt'><o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:1;mso-yfti-lastrow:yes;height:16.9pt'> <td width=520 valign=top style='width:389.85pt;padding:0in 5.4pt 0in 5.4pt; height:16.9pt'> <p class=TableParagraph align=center style='margin-top:.05pt;margin-right: .1in;margin-bottom:0in;margin-left:.1in;margin-bottom:.0001pt;text-align: center'><i><span lang=ES style='font-size:9.0pt;font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman"'>Figura II.- Categorías de los objetivos de las arquitecturas<o:p></o:p></span></i></p> </td> </tr> </table> </div> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><b style='mso-bidi-font-weight:normal'><span lang=ES>4.2. Desarrollar un modelo de arquitectura de un sistema computacional para una empresa ecuatoriana de seguros de salud orientado a la consolidación de la información.-</span></b><span lang=ES> De acuerdo a la metodología utilizada </span><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/BigData.2015.7363798&quot;,&quot;ISBN&quot;:&quot;978-1-4799-9926-2&quot;,&quot;abstract&quot;:&quot;Big Data constitutes an opportunity for companies to empower their analysis. However, at the moment there is no standard way for approaching Big Data projects. This, coupled with the complex nature of Big Data, is the cause that many Big Data projects fail or rarely obtain the expected return of investment. In this paper, we present a methodology to tackle Big Data projects in a systematic way, avoiding the aforementioned problems. To this end, we review the state of the art, identifying the most prominent problems surrounding Big Data projects, best practices and methods. Then, we define a methodology describing step by step how these techniques could be applied and combined in order to tackle the problems identified and increase the success rate of Big Data projects.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Tardio&quot;,&quot;given&quot;:&quot;Roberto&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Mate&quot;,&quot;given&quot;:&quot;Alejandro&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IEEE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2015&quot;,&quot;10&quot;]]},&quot;page&quot;:&quot;545-550&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;An iterative methodology for big data management&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=0a528cd7-63f3-4ac0-aa71-72fdb844a31c&quot;,&quot;http://www.mendeley.com/documents/?uuid=b4a1c6ca-8089-4033-a168-5d5f9672be5d&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[26]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[26]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[26]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES><span style='mso-no-proof:yes'>[26]</span></span><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES> se sigue el siguiente orden:</span></p> <p class=MsoBodyText style='margin-top:3.7pt;margin-right:0in;margin-bottom: 12.0pt;margin-left:0in;text-align:justify'><span lang=ES>i) Definir etapas de los <span class=GramE>datos.-</span> Aquí se identifican los requisitos de información funcionales y los requisitos de información no funcionales que sirven para el análisis del proyecto de Big Data; los requisitos funcionales establecen la estructura de datos y los requisitos no funcionales establecen las herramientas apropiadas para las labores de análisis. Entre los requisitos no funcionales se destacan: a) oportunidad-circunstancia de análisis que define la condición de tiempo desde la captura de datos hasta el análisis, b) exigencias en calidad de datos, y c) tiempo de consultas. Se adoptan cuatro tipos de labores de análisis representadas en la tabla I y se aplica los requisitos no funcionales: a) oportunidad - circunstancia en segundos, horas y días, b) exigencias en calidad de los datos en baja, media y alta, c) tiempo de consulta en post segundo y sub segundo. </span></p> <div align=center> <table class=MsoTableGrid border=0 cellspacing=0 cellpadding=0 width=533 style='border-collapse:collapse;mso-table-layout-alt:fixed;border:none; mso-yfti-tbllook:1184;mso-padding-alt:0in 5.4pt 0in 5.4pt;mso-border-insideh: none;mso-border-insidev:none'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;height:61.7pt'> <td width=533 valign=top style='width:399.75pt;padding:0in 5.4pt 0in 5.4pt; height:61.7pt'> <table class=MsoTableGrid border=1 cellspacing=0 cellpadding=0 width=496 style='margin-left:5.4pt;border-collapse:collapse;mso-table-layout-alt:fixed; border:none;mso-border-alt:solid windowtext .5pt;mso-yfti-tbllook:1184; mso-padding-alt:0in 5.4pt 0in 5.4pt'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes'> <td width=144 valign=top style='width:107.75pt;border:solid windowtext 1.0pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>Ambiente<o:p></o:p></span></b></p> </td> <td width=95 valign=top style='width:70.9pt;border:solid windowtext 1.0pt; border-left:none;mso-border-left-alt:solid windowtext .5pt;mso-border-alt: solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><b style='mso-bidi-font-weight:normal'><span lang=ES-EC style='font-size:9.0pt; mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>Circunstancia<o:p></o:p></span></b></p> </td> <td width=113 valign=top style='width:85.05pt;border:solid windowtext 1.0pt; border-left:none;mso-border-left-alt:solid windowtext .5pt;mso-border-alt: solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><b style='mso-bidi-font-weight:normal'><span lang=ES-EC style='font-size:9.0pt; mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>Calidad de Datos<o:p></o:p></span></b></p> </td> <td width=144 valign=top style='width:1.5in;border:solid windowtext 1.0pt; border-left:none;mso-border-left-alt:solid windowtext .5pt;mso-border-alt: solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><b style='mso-bidi-font-weight:normal'><span lang=ES-EC style='font-size:9.0pt; mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>Tiempo de consulta<o:p></o:p></span></b></p> </td> </tr> <tr style='mso-yfti-irow:1'> <td width=144 valign=top style='width:107.75pt;border:solid windowtext 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt: solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size: 11.0pt;mso-ansi-language:ES-EC'>Datos sin procesar<o:p></o:p></span></p> </td> <td width=95 valign=top style='width:70.9pt;border-top:none;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'> <o:p></o:p></span></p> </td> <td width=113 valign=top style='width:85.05pt;border-top:none;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>baja<o:p></o:p></span></p> </td> <td width=144 valign=top style='width:1.5in;border-top:none;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>post  segundo<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:2'> <td width=144 valign=top style='width:107.75pt;border:solid windowtext 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt: solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size: 11.0pt;mso-ansi-language:ES-EC'>Reportes<o:p></o:p></span></p> </td> <td width=95 valign=top style='width:70.9pt;border-top:none;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>días<o:p></o:p></span></p> </td> <td width=113 valign=top style='width:85.05pt;border-top:none;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>alta<o:p></o:p></span></p> </td> <td width=144 valign=top style='width:1.5in;border-top:none;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>post  segundo<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:3'> <td width=144 valign=top style='width:107.75pt;border:solid windowtext 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt: solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size: 11.0pt;mso-ansi-language:ES-EC'>Procesamiento en línea<o:p></o:p></span></p> </td> <td width=95 valign=top style='width:70.9pt;border-top:none;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>horas<o:p></o:p></span></p> </td> <td width=113 valign=top style='width:85.05pt;border-top:none;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>media<o:p></o:p></span></p> </td> <td width=144 valign=top style='width:1.5in;border-top:none;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>sub  segundo<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:4;mso-yfti-lastrow:yes'> <td width=144 valign=top style='width:107.75pt;border:solid windowtext 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt: solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size: 11.0pt;mso-ansi-language:ES-EC'>Tablero de gestión<o:p></o:p></span></p> </td> <td width=95 valign=top style='width:70.9pt;border-top:none;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>horas<o:p></o:p></span></p> </td> <td width=113 valign=top style='width:85.05pt;border-top:none;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>media<o:p></o:p></span></p> </td> <td width=144 valign=top style='width:1.5in;border-top:none;border-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windowtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC'>sub  segundo<o:p></o:p></span></p> </td> </tr> </table> </td> </tr> <tr style='mso-yfti-irow:1;mso-yfti-lastrow:yes;height:16.9pt'> <td width=533 valign=top style='width:399.75pt;padding:0in 5.4pt 0in 5.4pt; height:16.9pt'> <p class=TableParagraph align=center style='margin-top:.05pt;margin-right: .1in;margin-bottom:0in;margin-left:.1in;margin-bottom:.0001pt;text-align: center'><i><span lang=ES style='font-size:9.0pt;font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman"'>Tabla I.- Requerimientos no funcionales<o:p></o:p></span></i></p> </td> </tr> </table> </div> <p class=MsoBodyText style='margin-bottom:1.0pt'><span lang=ES>En este caso de la compañía de seguros de salud, el procesamiento en línea y el tablero de gestión son bajo tiempo/latencia y tienen la misma calidad de datos y, la misma circunstancia, basados en esto se definen 3 etapas, ver figura III. </span></p> <div align=center> <table class=TableNormal1 border=0 cellspacing=0 cellpadding=0 width=538 style='border-collapse:collapse;mso-table-layout-alt:fixed;mso-yfti-tbllook: 480;mso-padding-alt:0in 0in 0in 0in'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;mso-yfti-lastrow:yes; height:59.1pt'> <td width=538 valign=top style='width:5.6in;padding:0in 0in 0in 0in; height:59.1pt'> <table class=MsoTableGrid border=0 cellspacing=0 cellpadding=0 style='border-collapse:collapse;mso-table-layout-alt:fixed;border:none; mso-yfti-tbllook:1184;mso-padding-alt:0in 5.4pt 0in 5.4pt;mso-border-insideh: none;mso-border-insidev:none'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;height:42.05pt'> <td width=523 valign=top style='width:392.35pt;padding:0in 5.4pt 0in 5.4pt; height:42.05pt'> <p class=TableParagraph style='margin-top:.05pt'><span lang=ES-EC style='font-size:5.5pt;mso-bidi-font-size:11.0pt;mso-ansi-language:ES-EC; mso-fareast-language:ES-EC;mso-bidi-language:AR-SA;mso-no-proof:yes'><!--[if gte vml 1]><v:shape id="Imagen_x0020_9" o:spid="_x0000_i1027" type="#_x0000_t75" alt="Texto, Pizarra&#10;&#10;Descripción generada automáticamente" style='width:383pt;height:51pt;visibility:visible;mso-wrap-style:square'> <v:imagedata src="02_MIIUM23-03_Ecuador_Format.fld/image004.jpg" o:title="Texto, Pizarra&#10;&#10;Descripción generada automáticamente"/> </v:shape><![endif]--><![if !vml]><img width=383 height=51 src="http://www4.um.edu.uy/mailings/Imagenes/OJS_ING/Llerena004.jpg" alt="Texto, Pizarra&#10;&#10;Descripción generada automáticamente" v:shapes="Imagen_x0020_9"><![endif]></span><span lang=ES style='font-size:5.5pt;mso-bidi-font-size:11.0pt'><o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:1;mso-yfti-lastrow:yes;height:11.6pt'> <td width=523 valign=top style='width:392.35pt;padding:0in 5.4pt 0in 5.4pt; height:11.6pt'> <p class=TableParagraph align=center style='margin-top:.05pt;margin-right: .1in;margin-bottom:0in;margin-left:.1in;margin-bottom:.0001pt;text-align: center'><i><span lang=ES style='font-size:9.0pt;font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman"'>Figura III.- Camino de datos para la compañía<o:p></o:p></span></i></p> </td> </tr> </table> </td> </tr> </table> </div> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>ii) Gestionar las fuentes de datos.- Aquí se recolectan datos sin procesar y son cargados al sistema de archivos Big Data para que se almacenen en un clúster HDFS; de acuerdo a las oportunidades de la primera fase y la generación de las fuentes de datos, el responsable del Dataware House puede utilizar dos técnicas; la primera técnica es  Carga por lotes que utiliza en momentos que los datos están lejano del tiempo real o pueden estar empaquetados y la herramienta a utilizar es MapReduce; la segunda técnica es  Carga de transmisión que procesa los datos en tiempo real con la herramienta Apache Spark, y los datos en diferido los procesa con la herramienta Apache Flume. En ambas técnicas la conexión debe ser persistente con las fuentes de datos. </span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>Para el caso<span style='mso-spacerun:yes'>  </span>de una compañía de seguros de salud en el contexto ecuatoriano existen 12 fuentes de datos comunes y se recomienda el uso de la  Carga de transmisión para la base de datos, la técnica de la  Carga por lotes para los archivos que están en hojas electrónicas o procesadores de palabras (variedad), debido a que estos archivos se generan a diferentes velocidades (velocidad); la herramienta de almacenamiento frecuente es HDFS para el procesamiento de los archivos distribuidos en diferido semanalmente, Hadoop es el <i>framework</i> libre que permite ejecutar aplicaciones en clusters y MapReduce para el soporte de la computación paralela y distribuida de los macrodatos, Kafka es un estándar para recolectar mensajes como clics o comportamientos de usuarios en las páginas web, y finalmente Apache Streaming para procesar los conjuntos de mensajes.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>iii) Valorizar los <span class=GramE>datos.-</span><span style='mso-spacerun:yes'>  </span>Los datos sin procesar que están almacenados deben ser explorados para diseñar un modelo de datos multidimensional que genere valor a los datos, y estos datos recolectados en la segunda fase tienen entidades y relaciones no visibles o sin identificarse; el modelo de datos da a conocer estas entidades y relaciones. El Modelado Multidimensional es: simple porque divide el problema en hechos y dimensiones, es eficiente porque implementa un modelo conceptual, y se basa en un descubrimiento iterativo a través de la exploración de los datos sin procesar. El modelo diseñado debe responder incógnitas relevantes para la empresa.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>1) Explorar las fuentes de datos sin procesar: Aquí se identifica el requerimiento de información  para analizar los reclamos de clientes (hecho), mediante la  facturación promedio (medida) en función de: póliza, reclamos, renovación, pagos, clientes, dependientes, zona, agencia, fecha, tipo transacción (dimensiones); las consultas deben devolver la facturación, reclamos, pagos o renovaciones media agregada por fecha, y esta consulta permite plasmar con el requisito de información  analiza los reclamos de clientes , ver figura IV.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;margin-right:0in;margin-bottom: 12.0pt;margin-left:0in;text-align:justify'><span lang=ES>2) Integración: Aquí se propone integrar los varios modelos conceptuales que sean similares en sus hechos. La integración se repite iterativamente al momento de incorporar las fuentes de datos, entonces el resultado debe ser el modelo conceptual en Big Data, por esta razón se determinaron cuatro dimensiones y con ellas se realiza el análisis de las medidas.</span></p> <div align=center> <table class=TableNormal1 border=0 cellspacing=0 cellpadding=0 width=534 style='border-collapse:collapse;mso-table-layout-alt:fixed;mso-yfti-tbllook: 480;mso-padding-alt:0in 0in 0in 0in'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;mso-yfti-lastrow:yes; height:13.75pt'> <td width=534 valign=top style='width:400.3pt;padding:0in 0in 0in 0in; height:13.75pt'> <div align=center> <table class=MsoTableGrid border=0 cellspacing=0 cellpadding=0 style='border-collapse:collapse;mso-table-layout-alt:fixed;border:none; mso-yfti-tbllook:1184;mso-padding-alt:0in 5.4pt 0in 5.4pt;mso-border-insideh: none;mso-border-insidev:none'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;height:78.25pt'> <td width=427 valign=top style='width:320.2pt;padding:0in 5.4pt 0in 5.4pt; height:78.25pt'> <p class=TableParagraph align=center style='margin-top:.05pt;text-align: center'><span lang=ES-EC style='font-size:5.5pt;mso-bidi-font-size:11.0pt; mso-ansi-language:ES-EC;mso-fareast-language:ES-EC;mso-bidi-language:AR-SA; mso-no-proof:yes'><!--[if gte vml 1]><v:shape id="Imagen_x0020_2" o:spid="_x0000_i1026" type="#_x0000_t75" alt="Diagrama&#10;&#10;Descripción generada automáticamente" style='width:309pt;height:175pt;visibility:visible;mso-wrap-style:square'> <v:imagedata src="02_MIIUM23-03_Ecuador_Format.fld/image005.jpg" o:title="Diagrama&#10;&#10;Descripción generada automáticamente"/> </v:shape><![endif]--><![if !vml]><img width=309 height=175 src="http://www4.um.edu.uy/mailings/Imagenes/OJS_ING/Llerena005.jpg" alt="Diagrama&#10;&#10;Descripción generada automáticamente" v:shapes="Imagen_x0020_2"><![endif]></span><span lang=ES style='font-size:5.5pt;mso-bidi-font-size:11.0pt'><o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:1;mso-yfti-lastrow:yes;height:16.9pt'> <td width=427 valign=top style='width:320.2pt;padding:0in 5.4pt 0in 5.4pt; height:16.9pt'> <p class=TableParagraph align=center style='margin-top:.05pt;margin-right: .1in;margin-bottom:0in;margin-left:.1in;margin-bottom:.0001pt;text-align: center'><i><span style='font-size:9.0pt;font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-US'>Figura IV.- Modelo conceptual del Big Data Warehouse<o:p></o:p></span></i></p> </td> </tr> </table> </div> </td> </tr> </table> </div> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>3) Enriquecimiento de Big Data Warehouse: Con el modelo conceptual se puede entregar consultas a través de Apache Pig o Hive para consultar datos sin procesar, además este almacén es flexible, es decir, el modelo puede ser actualizado y puede ser enriquecido con dos alternativas como adicionar otros datos fuentes y aplicar minería de datos.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>Para el caso de una compañía de seguro de salud en general, para adicionar otros datos se consideran <i>brokers</i> (personas intermediarias en operaciones transaccionales en el ámbito financiero), vendedores independientes, sucursales, puntos de venta, puntos de reclamos y redes sociales. Para minería de datos se evidencia que hay diferentes tipos de clientes como personas o empresas, y se obtiene una nueva jerarquía, ejemplo, zona ’! tipo de cliente. En esta propuesta de investigación se aporta con una forma sistemática de reducir la complejidad en la integración de datos que se presenta como un desafío o problema de Big Data, además este estudio se enfoca en requisitos analíticos para cumplir y responder con criterios validados de datos relevantes.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>iv) Selección del almacén de datos. - Se selecciona un Big Data Warehouse para su posible y sencilla implementación, este almacén debe soportar herramientas de Inteligencia Empresarial y tener buena respuesta en la latencia de consultas. Para el caso de una compañía de seguros de salud, se selecciona Apache Hive con herramientas de Inteligencia Empresarial en memoria, que gestionan aplicaciones OLAP y Dashboard, debido a que en esta fase se debe implementar el modelo conceptual con el uso de la herramienta Hive luego, transformar los datos del esquema conceptual en herramienta de Apache Pig finalmente, es posible consultar y analizar el Big Data o pasar a una base de datos en memoria.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>v) Implementación visual del Big <span class=GramE>Data.-</span> Con todos los datos cargados en el almacén de datos, estos se utilizan a través de las herramientas de Inteligencia Empresarial para diseñar y visualizar tablas o cuadros de mando, debido a que estas herramientas generan más valor e interpretación a los datos en forma práctica y sencilla. En este caso de la compañía de seguros de salud se propone el uso de Power BI y con ello, la verificación de los siguientes indicadores:</span></p> <p class=MsoBodyText style='margin-top:3.7pt;margin-right:0in;margin-bottom: 0in;margin-left:.5in;margin-bottom:.0001pt;text-align:justify;text-indent:-.25in; mso-list:l0 level1 lfo2'><![if !supportLists]><span lang=ES style='font-family: Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span lang=ES>Indicadores área de ventas: mide la cantidad de prima (valor de pólizas) ingresado por mes, por agencia y por zona para determinar la región y agencia que está vendiendo más mensualmente, también se suele realizar revisiones anuales para promociones.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;margin-right:0in;margin-bottom: 0in;margin-left:.5in;margin-bottom:.0001pt;text-align:justify;text-indent:-.25in; mso-list:l0 level1 lfo2'><![if !supportLists]><span lang=ES style='font-family: Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span lang=ES>Indicadores de área de reclamos: mide la cantidad de prima de la póliza versus la cantidad de reclamos, esta comparación es anual de acuerdo con la vigencia que tenga la póliza, este indicador evalúa el comportamiento de ciertas carteras de clientes para brindar incentivos adicionales.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;margin-right:0in;margin-bottom: 0in;margin-left:.5in;margin-bottom:.0001pt;text-align:justify;text-indent:-.25in; mso-list:l0 level1 lfo2'><![if !supportLists]><span lang=ES style='font-family: Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span lang=ES>Indicadores del área de grupos: mide la cantidad de prima que ingresa por todo el grupo versus la cantidad de reclamos de todo el grupo, este indicador evalúa el comportamiento del grupo con el fin de renovarlo o mejorar sus beneficios.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;margin-right:0in;margin-bottom: 0in;margin-left:.5in;margin-bottom:.0001pt;text-align:justify;text-indent:-.25in; mso-list:l0 level1 lfo2'><![if !supportLists]><span lang=ES style='font-family: Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span lang=ES>Indicadores del área de retenciones: mide la cantidad de clientes que son retenidos o clientes con intención de cancelar su póliza.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;margin-right:0in;margin-bottom: 0in;margin-left:.5in;margin-bottom:.0001pt;text-align:justify;text-indent:-.25in; mso-list:l0 level1 lfo2'><![if !supportLists]><span lang=ES style='font-family: Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span lang=ES>Indicadores del área de renovaciones: mide la cantidad de clientes que solicitan actualización de cobertura durante su periodo de renovación anual.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;margin-right:0in;margin-bottom: 0in;margin-left:.5in;margin-bottom:.0001pt;text-align:justify;text-indent:-.25in; mso-list:l0 level1 lfo2'><![if !supportLists]><span lang=ES style='font-family: Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span lang=ES>Indicadores del área de pagos: mide los clientes, agencias o carteras que tienden a no cumplir con sus pagos a tiempo.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;margin-right:0in;margin-bottom: 0in;margin-left:.5in;margin-bottom:.0001pt;text-align:justify;text-indent:-.25in; mso-list:l0 level1 lfo2'><![if !supportLists]><span lang=ES style='font-family: Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span lang=ES>Indicadores de cancelación: mide las zonas, agencias o carteras que tienden a cancelar su cobertura antes de su finalización.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>Se especifica que los datos médicos son confidenciales, y como tal no se publican indicadores referentes a esta clase de datos, si algún investigador decide implementar el proyecto en la compañía de seguros solo puede ser utilizado por esa compañía.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;margin-right:0in;margin-bottom: 12.0pt;margin-left:0in;text-align:justify'><span lang=ES>La figura V representa el modelo de arquitectura basada en la metodología utilizada. En la <i>capa fuentes de datos</i> están los diferentes tipos de datos de las distintas áreas físicas que contiene la compañía, los detalles de estos datos fuentes fueron descritos en la sección  Datos generales en las compañías de seguros de salud , estos datos brutos sirven para a la fase de transformación. En la <i>capa transformación</i> se realiza el proceso ETL (<i>Extraction, Transformation and Load</i>) que requiere mucho tiempo de procesamiento; la extracción de datos de sistemas de origen que pueden ser bases de datos, archivos y otros tipos o diferentes sistemas, utiliza Hadoop, MapReduce y Kafka; la transformación de datos contiene una serie de funciones y reglas a través de las cuales deben pasar los datos, como limpiar, conciliar, dividir, seleccionar, combinar, estandarizar, eliminar datos duplicados entre otros, para ello se utiliza Apache Streaming; la carga de datos es el paso al almacén de datos desde la capa anterior, para esto se utiliza herramienta HDFS. Además, se realiza el proceso de envío al almacén de datos en un formato unificado, se utiliza Apache Pig y Hive. En la capa <i>herramientas</i> se nombra el software que se utiliza en todo el entorno desde datos fuentes hasta presentar los indicadores. En la capa <i>visualización</i> se presentan los datos sumarizados en reportes, tablas o gráficos mediante herramienta Power BI, el personal responsable aplica analítica de datos.</span></p> <div align=center> <table class=TableNormal1 border=0 cellspacing=0 cellpadding=0 width=541 style='border-collapse:collapse;mso-table-layout-alt:fixed;mso-yfti-tbllook: 480;mso-padding-alt:0in 0in 0in 0in'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;mso-yfti-lastrow:yes; height:77.9pt'> <td width=541 valign=top style='width:406.1pt;padding:0in 0in 0in 0in; height:77.9pt'> <table class=MsoTableGrid border=0 cellspacing=0 cellpadding=0 style='border-collapse:collapse;mso-table-layout-alt:fixed;border:none; mso-yfti-tbllook:1184;mso-padding-alt:0in 5.4pt 0in 5.4pt;mso-border-insideh: none;mso-border-insidev:none'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;height:56.65pt'> <td width=535 valign=top style='width:401.45pt;padding:0in 5.4pt 0in 5.4pt; height:56.65pt'> <p class=TableParagraph style='margin-top:.05pt;text-align:justify'><span lang=ES-EC style='font-size:5.5pt;mso-bidi-font-size:11.0pt;mso-ansi-language: ES-EC;mso-fareast-language:ES-EC;mso-bidi-language:AR-SA;mso-no-proof:yes'><!--[if gte vml 1]><v:shape id="Imagen_x0020_8" o:spid="_x0000_i1025" type="#_x0000_t75" alt="Diagrama&#10;&#10;Descripción generada automáticamente" style='width:390pt;height:63pt;visibility:visible;mso-wrap-style:square'> <v:imagedata src="02_MIIUM23-03_Ecuador_Format.fld/image006.jpg" o:title="Diagrama&#10;&#10;Descripción generada automáticamente" cropbottom="5085f"/> </v:shape><![endif]--><![if !vml]><img width=390 height=63 src="http://www4.um.edu.uy/mailings/Imagenes/OJS_ING/Llerena009.jpg" alt="Diagrama&#10;&#10;Descripción generada automáticamente" v:shapes="Imagen_x0020_8"><![endif]></span><span lang=ES style='font-size:5.5pt;mso-bidi-font-size:11.0pt'><o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:1;mso-yfti-lastrow:yes;height:3.1pt'> <td width=535 valign=top style='width:401.45pt;padding:0in 5.4pt 0in 5.4pt; height:3.1pt'> <p class=TableParagraph align=center style='margin-top:.05pt;margin-right: .1in;margin-bottom:0in;margin-left:.1in;margin-bottom:.0001pt;text-align: center'><i><span lang=ES style='font-size:9.0pt;font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman"'>Figura V.- Modelo de arquitectura en empresa ecuatoriana de seguros de salud<o:p></o:p></span></i></p> </td> </tr> </table> </td> </tr> </table> </div> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><b style='mso-bidi-font-weight:normal'><span lang=ES>4.3 Evaluar la metodología de estudio para establecer factores viables del modelo aplicable a una empresa ecuatoriana de seguros de salud mediante la contrastación de trabajos previos. - </span></b><span lang=ES>Para evaluar los factores viables del modelo sobre una empresa ecuatoriana de seguros de salud, se considera los siguientes niveles: Bajo, Medio y Alto, además si es o no aplicable en una empresa, ver tabla II. El 65% son de Alto factor aplicable (34 factores), entre los factores destacan las investigaciones que ofrecen seguridad, presentan arquitecturas/modelos, buscan disminuir costos, minimizar fraudes/errores, y utilizan herramientas de libre acceso. El 8% son de Medio factor aplicable (4 factores), entre los factores destacan los accesos a datos confidenciales del paciente, aunque la compañía de seguros es dueña de esta clase de datos, y son aplicables en la compañía. El 27% son de Bajo factor aplicable (14 factores), entre los factores se destaca la aplicación de algoritmos de Inteligencia Artificial porque no son objeto de estudio en esta investigación. El 73% de los factores son viables en la compañía de seguros de salud (38 factores) es decir, factores de Alta y Media aplicabilidad se consideran de utilidad en la empresa. El 27% de los factores son no viables (14 factores) es decir, factores de Baja aplicabilidad porque no se pueden utilizar en la empresa. Entre los 23 trabajos previos contrastados: 12 investigaciones son completamente aplicables, 8 investigaciones no son completamente aplicables, y 3 investigaciones no se pueden utilizar o aplicar.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><b style='mso-bidi-font-weight:normal'><span lang=ES>5. Discusión. - </span></b><span lang=ES>Nuestra investigación se enfoca en Big Data para empresas de seguros de salud, por esta razón se categorizaron diferentes arquitecturas computacionales. Se diseñó un modelo de arquitectura de un sistema computacional, y establecieron factores viables del modelo aplicable a una empresa ecuatoriana, todo esto basado en investigaciones científicas y relevantes. Los tres resultados de esta investigación están enlazados por la literatura analizada y utilizada. Entre las investigaciones, se conoció que cuatro de ellas aplican algoritmos de Inteligencia Artificial para encontrar patrones o tendencias, los datos provienen de una Big Data en área de seguros de salud. El modelo de arquitectura es teórico. Una limitante es que la empresa no tenga computación distribuida y otros recursos para cargar el Big Data Warehouse. Otras limitantes son desconfianza en el sistema, habilidades en la operación del sistema, fragmentación de datos y concientización sobre el uso de datos en salud. Las herramientas nombradas en el modelo de arquitectura son de libre acceso o licencia libre. No es objeto de esta investigación conocer los tiempos, ni talento humano, ni especificaciones de equipos requeridos para la implementación de la arquitectura.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>Para continuidad de esta investigación se propone un prototipo de Big Data, que tome como datos todas las fuentes de la empresa de seguros e implementada con las herramientas de software nombradas en el modelo de arquitectura.</span></p> <p class=MsoNormal><span lang=ES style='font-size:10.0pt'><o:p>&nbsp;</o:p></span></p> <div align=center> <table class=MsoTableGrid border=0 cellspacing=0 cellpadding=0 width=533 style='border-collapse:collapse;mso-table-layout-alt:fixed;border:none; mso-yfti-tbllook:1184;mso-padding-alt:0in 5.4pt 0in 5.4pt;mso-border-insideh: none;mso-border-insidev:none'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;height:49.9pt'> <td width=533 valign=top style='width:399.75pt;padding:0in 5.4pt 0in 5.4pt; height:49.9pt'> <table class=MsoTableGrid border=0 cellspacing=0 cellpadding=0 width=513 style='border-collapse:collapse;mso-table-layout-alt:fixed;border:none; mso-yfti-tbllook:1184;mso-padding-alt:0in 5.4pt 0in 5.4pt;mso-border-insidev: none'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes'> <td width=40 valign=top style='width:30.05pt;border-top:solid windowtext 1.5pt; border-left:none;border-bottom:solid windowtext 1.5pt;border-right:none; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><b style='mso-bidi-font-weight:normal'><span lang=ES-EC style='font-size:9.0pt; mso-ansi-language:ES-EC'>Art<o:p></o:p></span></b></p> </td> <td width=331 valign=top style='width:248.05pt;border-top:solid windowtext 1.5pt; border-left:none;border-bottom:solid windowtext 1.5pt;border-right:none; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><b style='mso-bidi-font-weight:normal'><span lang=ES-EC style='font-size:9.0pt; mso-ansi-language:ES-EC'>Factores<o:p></o:p></span></b></p> </td> <td width=76 valign=top style='width:56.7pt;border-top:solid windowtext 1.5pt; border-left:none;border-bottom:solid windowtext 1.5pt;border-right:none; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><b style='mso-bidi-font-weight:normal'><span lang=ES-EC style='font-size:9.0pt; mso-ansi-language:ES-EC'>Aplicable<o:p></o:p></span></b></p> </td> <td width=66 valign=top style='width:49.65pt;border-top:solid windowtext 1.5pt; border-left:none;border-bottom:solid windowtext 1.5pt;border-right:none; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><b style='mso-bidi-font-weight:normal'><span lang=ES-EC style='font-size:9.0pt; mso-ansi-language:ES-EC'>Empresa<o:p></o:p></span></b></p> </td> </tr> <tr style='mso-yfti-irow:1'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/FTC.2016.7821747&quot;,&quot;ISBN&quot;:&quot;978-1-5090-4171-8&quot;,&quot;abstract&quot;:&quot;The healthcare system consists of large volumes of data which are usually generated from diverse sources such as physicians' case notes, hospital admission notes, discharge summaries, pharmacies, insurance companies, medical imaging, laboratories, sensor based devices, genomics, social media as well as articles in medical journals. Healthcare data are however very complex and difficult to manage. This is as a result of the astronomical growth of healthcare data, the high speed at which these data are generated as well as the diversity of data types in healthcare. The capturing, storage, analysis and retrieval of health related data are rapidly shifting from paper based system towards digitization. However, the vast volume as well as the complexity of these data makes it difficult for the data to be processed and analyzed by traditional approaches and techniques. Consequently, technologies such as cloud computing and virtualization are now gradually used for processing massive data effectively and securely in healthcare. Hence, the healthcare system is swiftly becoming a big data industry. Thus, this paper examines the concept of big data in healthcare, its benefits and attendant challenges. This paper revealed that the fragmentation of healthcare data, ethical issues, usability issues as well as security and privacy issues are some of the factors impeding the successful implementation of big data in healthcare. 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padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Garantiza la seguridad y privacidad<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:2'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Adopta estándares<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:3'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/IDEA49133.2020.9170664&quot;,&quot;ISBN&quot;:&quot;9781728157184&quot;,&quot;abstract&quot;:&quot;At the present time, we can see around us everywhere big data is available like health sectors, insurance, share market, government organizations, social networking. Basically, big data refers to a huge amount of datasets and that dataset can be stored in the form of structured or unstructured format. Those datasets are producing in every second via a wide range of sources such as Facebook and Twitter. The big data has a basic three pillars- Volume (size), Variety (types) and Velocity (speed). Big data is a prediction platform where lots of dataset are available in the form of raw material, so we need an appropriate technique to process it and predict some valuable information for the present as well as future use. The sampling techniques are applicable to handle large datasets where all data sets are not possible to process at a time. In this situation, the sampling techniques play a very important role in the processing of big huge data. In this paper, we have proposed a parameter estimation model for big data to predict the mean size of large data by using the sampling technique. The proposed model can reduce the processing time as compare to the existing model because the sampling mechanism is to select small sample (n) from the entire datasets (N) and estimate the unknown parameters which are known as prediction results. Furthermore, we have presented the demonstration of the proposed algorithm on dynamic dataset and also compared result with simple random sampling (SRS) technique.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Alim&quot;,&quot;given&quot;:&quot;Abdul&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Shukla&quot;,&quot;given&quot;:&quot;Diwakar&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IDEA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2020&quot;]]},&quot;title&quot;:&quot;A parameter estimation model of big data setup&quot;,&quot;type&quot;:&quot;article-journal&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=21302b45-2345-488e-a333-4395ccb458d4&quot;,&quot;http://www.mendeley.com/documents/?uuid=119a424e-dbf6-4019-b123-f0d2b0245b2a&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[3]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[3]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[3]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[3]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Aplica algoritmo de predicción<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:4'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Presenta modelo de predicción<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:5'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICICCS.2016.7542360&quot;,&quot;ISBN&quot;:&quot;978-1-5090-2084-3&quot;,&quot;abstract&quot;:&quot;For the last century, the healthcare services and delivery including financing in India has been shrouded by life insurance challenges and importantly, shares key landmarks with general insurance. Many experts, practitioners and policy makers including researchers identified that, there is a big gap in the knowledge about innovations in public and private health financing and delivery. Apparent entails the necessity to alter the prevailing structure of the present health care services by applying Big Data Analytics. In this paper we'll discuss the various assorted implications, characteristics beside decrease in healthcare costs and how this new era of advanced and improved Data management is helping in Insurance Sector, more even we have to pay specific attention to the use cases which leads and drive new technology and eventually lead towards economic progress.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Gupta&quot;,&quot;given&quot;:&quot;Shalu&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Tripathi&quot;,&quot;given&quot;:&quot;Pooja&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICICCS&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;Iciccs&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;,&quot;2&quot;]]},&quot;page&quot;:&quot;64-69&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;An emerging trend of big data analytics with health insurance in India&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=700f2858-c030-4ca0-afdf-319814b0fa7e&quot;,&quot;http://www.mendeley.com/documents/?uuid=b272e54d-383c-4792-9388-be7c3377813f&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[5]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[5]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[5]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[5]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Disminuye costos<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:6'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Conoce comportamiento de pacientes y proveedor de salud <o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:7'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Presenta herramientas de software que utiliza<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:8'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Dirigido al sector público o privado<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:9'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICDE.2019.00209&quot;,&quot;ISBN&quot;:&quot;978-1-5386-7474-1&quot;,&quot;ISSN&quot;:&quot;10844627&quot;,&quot;abstract&quot;:&quot;Understanding the spread of diseases and the use of medicines is of practical importance for various organizations, such as medical providers, medical payers, and national governments. This study aims to detect the change in the prescription trends and to identify its cause through an analysis of Medical Insurance Claims (MICs), which comprise the specifications of medical fees charged to health insurers. Our approach is two-fold. (1) We propose a latent variable model that simulates the medication behavior of physicians to accurately reproduce monthly prescription time series from the MIC data, where prescription links between the diseases and medicines are missing. (2) We apply a state space model with intervention variables to decompose the monthly prescription time series into different components including seasonality and structural changes. Using a large dataset consisting of 3.5-year MIC records, we conduct experiments to evaluate our approach in terms of accuracy, usefulness, and efficiency. We also demonstrate three applications for our medical analysis.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Umemoto&quot;,&quot;given&quot;:&quot;Kazutoshi&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Goda&quot;,&quot;given&quot;:&quot;Kazuo&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICDE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2019&quot;,&quot;4&quot;]]},&quot;page&quot;:&quot;1928-1939&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;A Prescription Trend Analysis using Medical Insurance Claim Big Data&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;2019-April&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=516175e2-ee5d-4511-be25-ac7a6fb36be1&quot;,&quot;http://www.mendeley.com/documents/?uuid=ef9430fc-799c-44e2-a2e4-3e111577d5ce&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[7]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[7]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[7]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[7]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt;mso-ansi-language:ES-EC'> </span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Revisa la medicación de los pacientes <o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Medio<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:10'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>La compañía de seguros entrega el acceso a los datos <o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Medio<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:11'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Aplica un modelo de estados<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Medio<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:12'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICBDAA.2017.8284114&quot;,&quot;ISBN&quot;:&quot;978-1-5386-0790-9&quot;,&quot;abstract&quot;:&quot;Deliberate cheating by concealing and omitting facts while claiming from health insurance providers is considered as one of fraudulent activities in the health insurance domain which has led to significant amount of monetary loss to the providers. In view of the above, careful scanning of the submitted claim documents need to be conducted by the insurance companies in order to spot any discrepancy that indicates fraud. For this purpose, manual detection is neither easy nor practical as the claim documents received are plentiful and for diverse medical treatments. Hence, this paper shares the initial stage of our study which is aimed to propose an approach for detecting fraudulent health insurance claims by identifying correlation or association between some of the attributes on the claim documents. With the application of a data mining technique of association rules, this study advocates that the successful determination of correlated attributes can adequately address the discrepancies of data in fraudulent claims and thus reduce fraud in health insurance.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Kareem&quot;,&quot;given&quot;:&quot;Saba&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Binti Ahmad&quot;,&quot;given&quot;:&quot;Rohiza&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICBDA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2017&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;99-104&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Framework for the identification of fraudulent health insurance claims&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;2018-Janua&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=ffe5af9a-8128-4960-acdc-97af8c3f7383&quot;,&quot;http://www.mendeley.com/documents/?uuid=2fb1561c-82a1-4901-b617-bba095bc87cd&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[11]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[11]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[11]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[11]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Revisa los datos en los reclamos para reducir fraudes <o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:13'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Utiliza un framework <o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:14'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICoSTA48221.2020.1570615599&quot;,&quot;ISBN&quot;:&quot;9781728130835&quot;,&quot;abstract&quot;:&quot;Mobile phones are devices that always embedded in the society of Indonesian people, around 91% of Indonesians population use mobile phones and around 60% are smartphones. The most widely used smartphone application is social media, around 60% of Indonesia's population uses social media. The number of social media users in Indonesia opens up opportunities to analyze these social media data. Social media data mining, commonly referred to as text mining, can help stakeholders implement a data driven policy. In this paper will discuss the public perception on social media related to the issue of rising fees national health insurance or Badan Penyelenggara Jaminan Sosial (BPJS Kesehatan). The social media analyzed is Twitter because it can provide a comprehensive sentiment and discussion of an issue. We used Drone Emprit system to analyze social media data, Drone Emprit is a big data system that captures and analyzes discussion and sentiments on social media platforms, in this case Twitter. The data analyzed are all conversations that took place over 60 days in the time span of 22 September to 22 November 2019. During that time period 360,820 total conversations occurred with a percentage of sentiment of 91% showing negative sentiment, 5% positive and 4% neutral. This data shows that the majority of Indonesian people do not agree with the increase in fees of national health insurances (BPJS Kesehatan).&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Laagu&quot;,&quot;given&quot;:&quot;Muh Asnoer&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Setyo Arifin&quot;,&quot;given&quot;:&quot;Ajib&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICoSTA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2020&quot;]]},&quot;title&quot;:&quot;Analysis the Issue of Increasing National Health Insurance Rates&quot;,&quot;type&quot;:&quot;article-journal&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=44c8b715-1ad2-4442-a6e0-93690d930990&quot;,&quot;http://www.mendeley.com/documents/?uuid=f88023ce-3d39-4476-a4c8-80d48da39735&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[14]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[14]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[14]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[14]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Analiza los sentimientos expresados en una red social<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:15'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Usa herramientas libres<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:16'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Aplica algoritmo de Machine Learning<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:17'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/BigData.2015.7363798&quot;,&quot;ISBN&quot;:&quot;978-1-4799-9926-2&quot;,&quot;abstract&quot;:&quot;Big Data constitutes an opportunity for companies to empower their analysis. However, at the moment there is no standard way for approaching Big Data projects. This, coupled with the complex nature of Big Data, is the cause that many Big Data projects fail or rarely obtain the expected return of investment. In this paper, we present a methodology to tackle Big Data projects in a systematic way, avoiding the aforementioned problems. To this end, we review the state of the art, identifying the most prominent problems surrounding Big Data projects, best practices and methods. Then, we define a methodology describing step by step how these techniques could be applied and combined in order to tackle the problems identified and increase the success rate of Big Data projects.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Tardio&quot;,&quot;given&quot;:&quot;Roberto&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Mate&quot;,&quot;given&quot;:&quot;Alejandro&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IEEE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2015&quot;,&quot;10&quot;]]},&quot;page&quot;:&quot;545-550&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;An iterative methodology for big data management&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=0a528cd7-63f3-4ac0-aa71-72fdb844a31c&quot;,&quot;http://www.mendeley.com/documents/?uuid=b4a1c6ca-8089-4033-a168-5d5f9672be5d&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[26]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[26]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[26]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[26]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Usa metodología para aplicar análisis en Big data<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:18'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/JBHI.2015.2402692&quot;,&quot;ISSN&quot;:&quot;2168-2194&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Xie&quot;,&quot;given&quot;:&quot;Yang&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Schreier&quot;,&quot;given&quot;:&quot;Gunter&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Chang&quot;,&quot;given&quot;:&quot;David C W&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Neubauer&quot;,&quot;given&quot;:&quot;Sandra&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Liu&quot;,&quot;given&quot;:&quot;Ying&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Redmond&quot;,&quot;given&quot;:&quot;Stephen J&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Lovell&quot;,&quot;given&quot;:&quot;Nigel H&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IEEE Journal of Biomedical and Health Informatics&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;4&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2015&quot;,&quot;7&quot;]]},&quot;page&quot;:&quot;1224-1233&quot;,&quot;title&quot;:&quot;Predicting Days in Hospital Using Health Insurance Claims&quot;,&quot;type&quot;:&quot;article-journal&quot;,&quot;volume&quot;:&quot;19&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=9a09c1c7-d7e3-4666-a7f7-9fc4bea92c83&quot;,&quot;http://www.mendeley.com/documents/?uuid=ae489ff9-9081-4727-b5c0-5e1dc8d878d1&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[30]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[30]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[30]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[30]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Analiza los gastos anuales de hospitales <o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:19'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Utiliza un algoritmo de árbol de regresión<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:20'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/BigDataSecurityHPSCIDS52275.2021.00021&quot;,&quot;ISBN&quot;:&quot;978-1-6654-3927-5&quot;,&quot;abstract&quot;:&quot;After years of development, the traditional decision tree algorithm implementations represented by LightGBM has been very mature and widely used in various classification problems. However, when applying LightGBM to detecting fraud in health insurance data, we find that the performance of LightGBM is not ideal due to the large imbalance between the number of fraud examples and normal examples. To solve this problem, we propose a simple and effective LightGBM-based hard example mining algorithm (LHEM) for detecting health insurance fraud. Our motivation is to detect the large number of simple examples and the small number of hard examples in the dataset. Selecting these hard examples and discarding simple examples can balance the ratio of fraud and normal examples, thus improving the performance of the original model. We use the health insurance data collected in Zhejiang Jinhua to test our new method, and prove that the performance of our new method is better than LightGBM.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Yang&quot;,&quot;given&quot;:&quot;Wenyi&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Hu&quot;,&quot;given&quot;:&quot;Wenhui&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IEEE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;,&quot;5&quot;]]},&quot;page&quot;:&quot;57-62&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Research on Algorithm for Health Insurance Data Fraud Detection&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=b3dcc159-c6d8-40bd-89a0-579bfff76177&quot;,&quot;http://www.mendeley.com/documents/?uuid=d423330a-f0f8-42dc-bddb-e400c9038d8f&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[31]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[31]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[31]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[31]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Localiza los posibles fraudes en los reclamos<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:21'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Utiliza un algoritmo árbol de decisión<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:22'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/MITP.2013.55&quot;,&quot;ISSN&quot;:&quot;1520-9202&quot;,&quot;abstract&quot;:&quot;The healthcare sector deals with large volumes of electronic data related to patient services. This article describes two novel applications that leverage big data to detect fraud, abuse, waste, and errors in health insurance claims, thus reducing recurrent losses and facilitating enhanced patient care. The results indicate that claim anomalies detected using these applications help private health insurance funds recover hidden cost overruns that aren't detectable using transaction processing systems. This article is part of a special issue on leveraging big data and business analytics. © 1999-2012 IEEE.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Srinivasan&quot;,&quot;given&quot;:&quot;Uma&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Arunasalam&quot;,&quot;given&quot;:&quot;Bavani&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IT Professional&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;6&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2013&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;21-28&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Leveraging Big Data Analytics to Reduce Healthcare Costs&quot;,&quot;type&quot;:&quot;article-journal&quot;,&quot;volume&quot;:&quot;15&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=fb7eaa51-522d-4c16-a146-0a81e95219dd&quot;,&quot;http://www.mendeley.com/documents/?uuid=d9f2a937-2cf1-4031-973d-7eb4d64e10e5&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[32]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[32]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[32]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[32]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Descubre fraudes, derroches o sobre precios <o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:23'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Genera modelos predictivos e inteligencia de negocios<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:24'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Alerta al personal administrativo<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:25'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/ICSCSE.2016.0159&quot;,&quot;ISBN&quot;:&quot;978-1-5090-5530-2&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Jiang&quot;,&quot;given&quot;:&quot;Lesheng&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICSCSE&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2016&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;590-593&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Quantitative Model of Insurance Risk Management System Based on Big Data&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=24ea7482-0db2-43aa-a2ca-09ddb113d8b0&quot;,&quot;http://www.mendeley.com/documents/?uuid=6d7c2958-6cd1-4af7-8795-6ecd401f892d&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[33]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[33]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[33]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[33]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Clasifica enfermedades<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:26'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Aplica algoritmo supervisado de inteligencia artificial<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:27'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/SUMMA48161.2019.8947605&quot;,&quot;ISBN&quot;:&quot;978-1-7281-4911-0&quot;,&quot;abstract&quot;:&quot;Big data characterizing the quality of medical health care services provided to the population contain anomaly observations, which are either results of technical errors in filling databases, or falsified data. In the context of insurance medicine, the actual task is to identify such observation. The paper presents an approach to identify outliers in the database of medical health care information system based on the combination of Isolating Forest algorithm and subsequent neural network classifier.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Sysoev&quot;,&quot;given&quot;:&quot;Anton&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Scheglevatych&quot;,&quot;given&quot;:&quot;Roman&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;SUMMA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2019&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;359-363&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Combined Approach to Detect Anomalies in Health Care Datasets&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=cbb8c16a-2271-465a-a243-004c394ceae7&quot;,&quot;http://www.mendeley.com/documents/?uuid=96b994ae-efa6-4157-a869-7dc0dfaee3cf&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[34]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[34]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[34]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[34]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Encuentra errores de datos o datos falsificados; <o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:28'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Combina un algoritmo de árbol con un clasificador neuronal<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:29'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/3368756.3369103&quot;,&quot;ISBN&quot;:&quot;9781450362894&quot;,&quot;abstract&quot;:&quot;Clustering techniques aim to discover groupings of a set of patterns or data and are widely used in any discipline that involves analysis of multivariate data. Their applications in different fields are multiple and diverse. They can also be used in health insurance, which is an important part of healthcare. The use of clustering techniques in this field will provide opportunities and solutions for decision makers in order to monitor the insurance coverage in general and health insurance in particular. The aim of this paper is to create clusters of the insured population by means of an unsupervised machine learning algorithm, which is a partitional clustering technique that has proved to be efficient and fast, which is K-means. We present and discuss the results obtained by using this clustering method.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Zahi&quot;,&quot;given&quot;:&quot;Sara&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Achchab&quot;,&quot;given&quot;:&quot;Boujemâa&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICSCA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2019&quot;,&quot;10&quot;,&quot;2&quot;]]},&quot;page&quot;:&quot;1-6&quot;,&quot;publisher&quot;:&quot;ACM&quot;,&quot;publisher-place&quot;:&quot;New York, NY, USA&quot;,&quot;title&quot;:&quot;Clustering of the population benefiting from health insurance&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=fa51bfbf-a5d6-4976-ba6a-4fb3c0b785d8&quot;,&quot;http://www.mendeley.com/documents/?uuid=c95d074b-acd6-42a7-870b-487a6afb316e&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[35]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[35]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[35]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><span style='mso-no-proof:yes'>[35]</span></span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Evidencia patrones de enfermedades<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:30'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Aplica algoritmo no supervisado de inteligencia artificial<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:31'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1109/CHASE.2017.81&quot;,&quot;ISBN&quot;:&quot;978-1-5090-4722-2&quot;,&quot;abstract&quot;:&quot;Public health researchers increasingly recognize that to advance their field they must grapple with the availability of increasingly large (i.e., thousands of variables) traditional population-level datasets (e.g., electronic medical records), while at the same time integrating additional large datasets (e.g., data on genomics, the microbiome, environmental exposures, socioeconomic factors, and health behaviors). Leveraging these multiple forms of data might well provide unique and unexpected discoveries about the determinants of health and wellbeing. However, we are in the very early stages of advancing the techniques required to understand and analyze big population-level data for public health research. To address this problem, this paper describes how we propose that big data can be efficiently used for public health discoveries. We show that data analytics techniques traditionally employed in public health studies are not up to the task of the data we now have in hand. Instead we present techniques adapted from big data visualization and analytics approaches used in other domains that can be used to answer important public health questions utilizing these existing and new datasets. Our findings are based on an exploratory big data case study carried out in San Diego County, California where we analyzed thousands of variables related to health to gain interesting insights on the determinants of several health outcomes, including life expectancy and anxiety disorders. These findings provide a promising early indication that public health research will benefit from the larger set of activities in contemporary big data research.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Katsis&quot;,&quot;given&quot;:&quot;Yannis&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Balac&quot;,&quot;given&quot;:&quot;Natasha&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;IEEE/ACM&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2017&quot;,&quot;7&quot;]]},&quot;page&quot;:&quot;222-231&quot;,&quot;publisher&quot;:&quot;IEEE&quot;,&quot;title&quot;:&quot;Big Data Techniques for Public Health&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=fd1bdeb5-9fc2-4f84-9459-12f4de72d3ea&quot;,&quot;http://www.mendeley.com/documents/?uuid=73aef023-3d17-4b7d-8f19-a3634b18c827&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[36]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[36]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[36]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><span style='mso-no-proof:yes'>[36]</span></span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'>Determina causas que inciden en la salud<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES style='font-size:9.0pt'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí</span><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:32'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'>Aplica algoritmo de Machine Learning<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES style='font-size:9.0pt'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES style='font-size:9.0pt'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:33'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/3348400.3348414&quot;,&quot;ISBN&quot;:&quot;9781450371674&quot;,&quot;abstract&quot;:&quot;Big Data is the biggest emerging trend and promise in today's technology-driven world. It is continuing to create a lot of buzz in not only the field of technology, but across the world. It promises substantial involvements, vast changes, modernizations, and integration with and within people's ongoing life. It makes the world more demanding and helps with making prompt and appropriate decisions in real time. This paper aims to provide a comprehensive analysis of the health industry and health care system in Australia that are relevant to the consequences formed by Big Data. This paper primarily uses a secondary research analysis method to provide a wide-ranging investigation into the positive and negative consequences of health issues relevant to Big Data, the architects of those consequences, and those overstated by the consequences. The secondary resources are subject to journal articles, reports, conference proceedings, media articles, corporation-based documents, blogs and other appropriate information. In the future, the investigation will continue by employing Mixed Methodology (Qualitative and Quantitative) in relation to Big Data usage in the Australian Health industry. The paper initially finds that Big Data is an evidence source in health care and provides useful insight into the Australian healthcare system. It is steadily reducing the cost of the Australian healthcare system and improving patients' outcomes in Australia. Big data can not only improve the affairs between public and health enterprises, but can also make life better by increasing efficiency and modernization.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Karim&quot;,&quot;given&quot;:&quot;Shakir&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Gide&quot;,&quot;given&quot;:&quot;Ergun&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICMSTTL&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2019&quot;]]},&quot;page&quot;:&quot;34-38&quot;,&quot;publisher&quot;:&quot;ACM Press&quot;,&quot;publisher-place&quot;:&quot;New York, New York, USA&quot;,&quot;title&quot;:&quot;The Impact of Big Data on Health Care Services in Australia&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=c37c0737-c685-43ea-bfbf-52fb2be7b7f7&quot;,&quot;http://www.mendeley.com/documents/?uuid=2eb4af3a-baf7-490a-85c8-ae35cec18386&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[37]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[37]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[37]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}<span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[37]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Análisis de salud<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:34'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Predice riesgos<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:35'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/2684103.2684178&quot;,&quot;ISBN&quot;:&quot;9781450330084&quot;,&quot;abstract&quot;:&quot;The current National health insurance in Taiwan is a system widely praised by other countries, but the rising healthcare expenditure has been a serious financial issue in recent years. Many scholars have adopted the National Health Insurance Research Database (NHIRD) for all sorts of research studies. However, few have conducted research from the perspective of multiple-level stakeholders, which remains to be a worth exploring research direction. This research addressed the above issue by constructing a Hierarchical linear modeling (HLM) based on the available data in NHIRD in order to identify key factors influencing health spending. A future work is to conduct an empirical study to validate the HLM model, which can be also good reference for future insightful research on multiple-stakeholder perspective.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Chen&quot;,&quot;given&quot;:&quot;Ying-Kai&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Tao&quot;,&quot;given&quot;:&quot;Yu-Hui&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICAMCM&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2014&quot;,&quot;12&quot;,&quot;8&quot;]]},&quot;page&quot;:&quot;353-355&quot;,&quot;publisher&quot;:&quot;ACM&quot;,&quot;publisher-place&quot;:&quot;New York, NY, USA&quot;,&quot;title&quot;:&quot;Analyzing the Healthcare Expenditure of National Health Insurance&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=6c815675-5dc4-4fad-8a0b-fbb27993f3c2&quot;,&quot;http://www.mendeley.com/documents/?uuid=5f7c7fbd-383b-4ffd-81fa-119813c196f7&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[38]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[38]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[38]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[38]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Conoce el gasto en salud<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:36'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Presenta un framework<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:37'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/2487575.2488205&quot;,&quot;ISBN&quot;:&quot;9781450321747&quot;,&quot;abstract&quot;:&quot;The role of big data in addressing the needs of the present healthcare system in US and rest of the world has been echoed by government, private, and academic sectors. There has been a growing emphasis to explore the promise of big data analytics in tapping the potential of the massive healthcare data emanating from private and government health insurance providers. While the domain implications of such collaboration are well known, this type of data has been explored to a limited extent in the data mining community. The objective of this paper is two fold: first, we introduce the emerging domain of big\&quot;healthcare claims data to the KDD community, and second, we describe the success and challenges that we encountered in analyzing this data using state of art analytics for massive data. Specifically, we translate the problem of analyzing healthcare data into some of the most well-known analysis problems in the data mining community, social network analysis, text mining, and temporal analysis and higher order feature construction, and describe how advances within each of these areas can be leveraged to understand the domain of healthcare. Each case study illustrates a unique intersection of data mining and healthcare with a common objective of improving the cost-care ratio by mining for opportunities to improve healthcare operations and reducing what seems to fall under fraud, waste, and abuse.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Chandola&quot;,&quot;given&quot;:&quot;Varun&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Sukumar&quot;,&quot;given&quot;:&quot;Sreenivas R.&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ACM&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2013&quot;,&quot;8&quot;,&quot;11&quot;]]},&quot;page&quot;:&quot;1312-1320&quot;,&quot;publisher&quot;:&quot;ACM&quot;,&quot;publisher-place&quot;:&quot;New York, NY, USA&quot;,&quot;title&quot;:&quot;Knowledge discovery from massive healthcare claims data&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;Part F1288&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=c97e28ec-e7f3-495f-8b6e-e43962bc7901&quot;,&quot;http://www.mendeley.com/documents/?uuid=e19df889-dae7-43fd-8f13-2171ad3ac96b&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[39]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[39]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[39]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[39]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Identifica fraudes<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:38'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Conoce interesados en el sistema de salud<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:39'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/3297730.3297743&quot;,&quot;ISBN&quot;:&quot;9781450365826&quot;,&quot;abstract&quot;:&quot;With the rise of the concept of financial technology, financial and technology gradually in-depth integration, scientific and technological means to become financial product innovation, improve financial efficiency and reduce financial transaction costs an important driving force. In this context, the new technology platform is from the business philosophy, business model, technical means, sales, internal management and other dimensions to re-shape the financial industry. In this paper, the existing big data platform architecture technology innovation, adding space-time data elements, combined with the insurance industry for practical analysis, put forward a meaningful product circle and customer circle.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Liu&quot;,&quot;given&quot;:&quot;Yi&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Peng&quot;,&quot;given&quot;:&quot;Jiawen&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;BDET&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2018&quot;]]},&quot;page&quot;:&quot;31-35&quot;,&quot;publisher&quot;:&quot;ACM Press&quot;,&quot;publisher-place&quot;:&quot;New York, New York, USA&quot;,&quot;title&quot;:&quot;Big Data Platform Architecture&quot;,&quot;type&quot;:&quot;article-journal&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=2d3332aa-b375-416d-a50e-9bb44ee5473d&quot;,&quot;http://www.mendeley.com/documents/?uuid=9d5228ab-6ba5-46b7-b99b-5cbc0a4c0cdb&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[40]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[40]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[40]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[40]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Controla gastos financieros<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:40'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Presenta modelo basado en Big data<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:41'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Presenta herramientas de software para su uso<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:42'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/1835804.1835816&quot;,&quot;ISBN&quot;:&quot;9781450300551&quot;,&quot;abstract&quot;:&quot;Health insurance costs across the world have increased alarmingly in recent years. A major cause of this increase are payment errors made by the insurance companies while processing claims. These errors often result in extra administrative effort to re-process (or rework) the claim which accounts for up to 30% of the administrative staff in a typical health insurer. We describe a system that helps reduce these errors using machine learning techniques by predicting claims that will need to be reworked, generating explanations to help the auditors correct these claims, and experiment with feature selection, concept drift, and active learning to collect feedback from the auditors to improve over time. We describe our framework, problem formulation, evaluation metrics, and experimental results on claims data from a large US health insurer. We show that our system results in an order of magnitude better precision (hit rate) over existing approaches which is accurate enough to potentially result in over $15-25 million in savings for a typical insurer. We also describe interesting research problems in this domain as well as design choices made to make the system easily deployable across health insurance companies. © 2010 ACM.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Kumar&quot;,&quot;given&quot;:&quot;Mohit&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Ghani&quot;,&quot;given&quot;:&quot;Rayid&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ACM&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2010&quot;]]},&quot;page&quot;:&quot;65&quot;,&quot;publisher&quot;:&quot;ACM Press&quot;,&quot;publisher-place&quot;:&quot;New York, New York, USA&quot;,&quot;title&quot;:&quot;Data mining to predict and prevent errors in health insurance claims processing&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=a98a7189-7ce0-41fc-80af-715ffb3e637f&quot;,&quot;http://www.mendeley.com/documents/?uuid=66e92bb0-5e7b-4191-9e80-4d26c43cfc8f&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[41]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[41]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[41]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[41]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Previene errores<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:43'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Presenta estructura del modelo<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Medio<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:44'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Aplica algoritmo de Machine Learning<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:45'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/3451471.3451490&quot;,&quot;ISBN&quot;:&quot;9781450388955&quot;,&quot;abstract&quot;:&quot;This study estimates the effect of reducing social insurance contributions on firm insurance behavior and firm activity using a standard difference-in-differences strategy and rich tax survey data from China. This quantitative approach contributes to the literature by providing better estimates of causality, from more perspectives, thus delivering more credible conclusions. We find that reducing social insurance contributions can affect firm insurance behavior through compliance. On the one hand, lowering the contribution rate has led to an increase in compliance. On the other hand, reducing the policy contribution rate has had a positive impact on companies' per capita salary and investment, which helps to achieve the policy goals of stabilizing employment, promoting investment, and sustaining economic growth.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Chen&quot;,&quot;given&quot;:&quot;Yuxiao&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;She&quot;,&quot;given&quot;:&quot;Kaiwen&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Zhao&quot;,&quot;given&quot;:&quot;Shaoyang&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ICSEIM&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issue&quot;:&quot;24&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2021&quot;,&quot;1&quot;,&quot;16&quot;]]},&quot;page&quot;:&quot;113-117&quot;,&quot;publisher&quot;:&quot;ACM&quot;,&quot;publisher-place&quot;:&quot;New York, NY, USA&quot;,&quot;title&quot;:&quot;Social Insurance Contribution Rate Reduction and Firm Activity Evidence&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=8df11df6-c333-4799-a989-7a97164625ec&quot;,&quot;http://www.mendeley.com/documents/?uuid=09f5b4c2-2e6b-4523-8351-da1f96c29bd5&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[42]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[42]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[42]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[42]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Reduce la tasa de contribución<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:46'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Modelo matemático<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:47'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/2808719.2816981&quot;,&quot;ISBN&quot;:&quot;9781450338530&quot;,&quot;abstract&quot;:&quot;In 10 years, one third of the population in developed countries will be 60-years or older. 90% of seniors want to age in their own homes, not a facility. Families want to know that their aging parents are healthy and safe. It is extremely challenging for seniors with chronic conditions to manage their day-to-day medical needs and prevent medical emergencies, e.g. stroke, fall, etc. With the rapid growth of Internet of Things, increasing popularity of wearable medical devices or monitor devices, and other sensors at home, the opportunity for senior health management and prevention of medical emergency at home has never been better with big data platform, integration of massive and diverse data sources, e.g. medical charts from doctor's office, prescription information from pharmacies, claims data from insurance companies, more importantly, real-time data stream from Internet-of-things, wearable devices, and other vital and sensor data at home; and even more critically, real-time alerts of various risks from predictive analytics connected with providers, care givers and family members. In this paper, we will analyze the scope of the problem, and present current status and challenges in this area. Secondly we will propose a prototype schema to address this problem through Internet of things and real-time big data predictive analytics. Finally we will discuss some technical and non-technical challenges observed.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Deng&quot;,&quot;given&quot;:&quot;Xin&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Wu&quot;,&quot;given&quot;:&quot;Donghui&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;ACM&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2015&quot;,&quot;9&quot;,&quot;9&quot;]]},&quot;page&quot;:&quot;674-674&quot;,&quot;publisher&quot;:&quot;ACM&quot;,&quot;publisher-place&quot;:&quot;New York, NY, USA&quot;,&quot;title&quot;:&quot;Senior health management through internet of things and real-time big data analytics&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=2afd6b6e-6750-4516-8718-47589e3a7ba7&quot;,&quot;http://www.mendeley.com/documents/?uuid=b14e2bde-3d96-4a83-9ff6-9dc81ee60cb0&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[43]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[43]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[43]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[43]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Seguimiento de salud<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:48'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Aplica analítica predictiva<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Bajo<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>No<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:49'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/3090354.3090388&quot;,&quot;ISBN&quot;:&quot;9781450348522&quot;,&quot;abstract&quot;:&quot;In recent year, cloud computing and big data have become the hottest research topics which attract the attention of researchers due to its potential to provide major benefits to the industry and the community. Currently, the Organizations have a new option to outsource their massive data in the cloud without having to worry about the size of data or the capacity of memory. However, moving confidential and sensitive data from trusted domain of the data owners to public cloud will cause various security and privacy risks. Furthermore, the increasing amount of big data outsourced in the cloud increases the chance of breaching the privacy and security of these data. Despite all the research that has been done in this area, big data storage security and privacy remains one of the major concerns of organizations that adopt the cloud computing and big data technologies. Thus to ensure better data security we need for focus on two major problems which are the access control and encryption policies. In this paper, we propose a new hybrid model that enhances the security and privacy of big data shared in cloud environment using an access control scheme based on KP-ABE and authentication system. Our approach provides a flexible fine-grained access control of big data stored and shared in the cloud computing such that the encrypted data can only be accessed by authorized users.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Sara&quot;,&quot;given&quot;:&quot;Amghar&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Yassine&quot;,&quot;given&quot;:&quot;Tabaa&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;iCBDCA&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2017&quot;,&quot;3&quot;,&quot;29&quot;]]},&quot;page&quot;:&quot;1-4&quot;,&quot;publisher&quot;:&quot;ACM&quot;,&quot;publisher-place&quot;:&quot;New York, NY, USA&quot;,&quot;title&quot;:&quot;Secure confidential big data sharing in cloud computing&quot;,&quot;type&quot;:&quot;paper-conference&quot;,&quot;volume&quot;:&quot;Part F1294&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=e1053e92-3ffb-434b-834e-794ff36deb04&quot;,&quot;http://www.mendeley.com/documents/?uuid=5d3758e4-cf88-4f60-ab6a-85374c606613&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[44]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[44]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[44]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[44]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt;mso-ansi-language:ES-EC'> </span><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Preserva la confidencialidad<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:50'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES style='font-size:9.0pt'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Presenta arquitectura<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:51'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><!--[if supportFields]><span lang=ES style='font-size: 9.0pt'><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>ADDIN CSL_CITATION {&quot;citationItems&quot;:[{&quot;id&quot;:&quot;ITEM-1&quot;,&quot;itemData&quot;:{&quot;DOI&quot;:&quot;10.1145/1125451.1125484&quot;,&quot;ISBN&quot;:&quot;1595932984&quot;,&quot;abstract&quot;:&quot;This Experience Report describes the challenges of evaluating the usability of a Web-based collaborative health insurance benefits planning application. The application was created by researchers at the National Institutes of Health and the University of Michigan.&quot;,&quot;author&quot;:[{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Kantner&quot;,&quot;given&quot;:&quot;Laurie&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;},{&quot;dropping-particle&quot;:&quot;&quot;,&quot;family&quot;:&quot;Goold&quot;,&quot;given&quot;:&quot;Susan Dorr&quot;,&quot;non-dropping-particle&quot;:&quot;&quot;,&quot;parse-names&quot;:false,&quot;suffix&quot;:&quot;&quot;}],&quot;container-title&quot;:&quot;CHI&quot;,&quot;id&quot;:&quot;ITEM-1&quot;,&quot;issued&quot;:{&quot;date-parts&quot;:[[&quot;2006&quot;,&quot;4&quot;,&quot;21&quot;]]},&quot;page&quot;:&quot;141-146&quot;,&quot;publisher&quot;:&quot;ACM&quot;,&quot;publisher-place&quot;:&quot;New York, NY, USA&quot;,&quot;title&quot;:&quot;Web tool for health insurance design by small groups&quot;,&quot;type&quot;:&quot;paper-conference&quot;},&quot;uris&quot;:[&quot;http://www.mendeley.com/documents/?uuid=177a7b5f-fb73-44b4-88cc-3c88a44ad022&quot;,&quot;http://www.mendeley.com/documents/?uuid=5ac42344-9a78-4238-9a2e-7cb214f7e7c9&quot;]}],&quot;mendeley&quot;:{&quot;formattedCitation&quot;:&quot;[45]&quot;,&quot;plainTextFormattedCitation&quot;:&quot;[45]&quot;,&quot;previouslyFormattedCitation&quot;:&quot;[45]&quot;},&quot;properties&quot;:{&quot;noteIndex&quot;:0},&quot;schema&quot;:&quot;https://github.com/citation-style-language/schema/raw/master/csl-citation.json&quot;}</span><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-separator'></span></span><![endif]--><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC;mso-no-proof: yes'>[45]</span><!--[if supportFields]><span lang=ES style='font-size:9.0pt'><span style='mso-element:field-end'></span></span><![endif]--><span lang=ES style='font-size:9.0pt'><o:p></o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Preserva la seguridad<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt:solid windowtext .5pt; mso-border-bottom-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto<o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.0pt;mso-border-top-alt:solid windowtext .5pt;mso-border-top-alt: solid windowtext .5pt;mso-border-bottom-alt:solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:52;mso-yfti-lastrow:yes'> <td width=40 valign=top style='width:30.05pt;border:none;border-bottom: solid windowtext 1.5pt;mso-border-top-alt:solid windowtext .5pt;padding: 0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'><o:p>&nbsp;</o:p></span></p> </td> <td width=331 valign=top style='width:248.05pt;border:none;border-bottom: solid windowtext 1.5pt;mso-border-top-alt:solid windowtext .5pt;padding: 0in 5.4pt 0in 5.4pt'> <p class=MsoNormal><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language: ES-EC'>Presenta índices de gestión<o:p></o:p></span></p> </td> <td width=76 valign=top style='width:56.7pt;border:none;border-bottom:solid windowtext 1.5pt; mso-border-top-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Alto <o:p></o:p></span></p> </td> <td width=66 valign=top style='width:49.65pt;border:none;border-bottom: solid windowtext 1.5pt;mso-border-top-alt:solid windowtext .5pt;padding: 0in 5.4pt 0in 5.4pt'> <p class=MsoNormal align=center style='text-align:center'><span lang=ES-EC style='font-size:9.0pt;mso-ansi-language:ES-EC'>Sí<o:p></o:p></span></p> </td> </tr> </table> </td> </tr> <tr style='mso-yfti-irow:1;mso-yfti-lastrow:yes;height:16.9pt'> <td width=533 valign=top style='width:399.75pt;padding:0in 5.4pt 0in 5.4pt; height:16.9pt'> <p class=TableParagraph align=center style='margin-top:.05pt;margin-right: .1in;margin-bottom:0in;margin-left:.1in;margin-bottom:.0001pt;text-align: center'><i><span lang=ES style='font-size:9.0pt;font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman"'>Tabla II.- Factores viables<o:p></o:p></span></i></p> </td> </tr> </table> </div> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><b style='mso-bidi-font-weight:normal'><span lang=ES>6. Conclusiones. - </span></b><span lang=ES>En este trabajo se obtienen las siguientes categorías, prevención de enfermedades, causas de reclamos, expresión de sentimientos, fraudes en reclamos, predicción de enfermedades y seguridad de datos, desde las arquitecturas computacionales para seguros de la salud mediante una revisión de literatura relevante. Se desarrolla un modelo de arquitectura de un sistema computacional que consiste en 4 capas: Capa Fuentes de Datos, Capa Transformación, Capa Herramientas y Capa Visualización, el diseño es para una empresa ecuatoriana de seguros de salud y está orientado a la consolidación de la información.</span></p> <p class=MsoBodyText style='margin-top:3.7pt;text-align:justify'><span lang=ES>Se evalúa la metodología de estudio mediante la contrastación de trabajos previos, y se conoce que el 73% de los factores (Alto y Medio) son viables en una empresa ecuatoriana de seguros de salud, es decir, son de aplicabilidad para la empresa. El 27% de los factores (Bajo) son no viables, es decir no se pueden aplicar en la empresa. El aporte de este trabajo permite determinar aquellos factores viables que determinan la aplicabilidad del modelo a empresas de seguros de salud nacionales o extranjeras inclusive. </span></p> <span lang=ES style='font-size:11.0pt;font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman";mso-ansi-language:ES;mso-fareast-language: ES;mso-bidi-language:ES'><br clear=all style='mso-special-character:line-break; page-break-before:always'> </span> <p class=MsoNormal><span lang=ES style='font-size:10.0pt'><o:p>&nbsp;</o:p></span></p> <p class=MsoNormal style='margin-top:4.3pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt'><b style='mso-bidi-font-weight:normal'><span style='mso-ansi-language:EN-US'>7. Referencias<o:p></o:p></span></b></p> <p class=MsoNormal style='margin-left:21.3pt;text-align:justify;text-indent: -21.3pt;mso-layout-grid-align:none'><!--[if supportFields]><span lang=ES><span style='mso-element:field-begin;mso-field-lock:yes'></span></span><span style='mso-ansi-language:EN-US'>ADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY </span><span lang=ES><span style='mso-element:field-separator'></span></span><![endif]--><span style='font-size:10.0pt;mso-bidi-font-size:12.0pt;mso-ansi-language:EN-US; mso-no-proof:yes'>[1]<span style='mso-tab-count:1'>   </span>I. Olaronke and O. Oluwaseun,  Big data in healthcare, in <i>FTC</i>, Dec. 2016, no. December, pp. 1152 1157, doi: 10.1109/FTC.2016.7821747.<o:p></o:p></span></p> <p class=MsoNormal style='margin-left:21.3pt;text-align:justify;text-indent: -21.3pt;mso-layout-grid-align:none'><span style='font-size:10.0pt;mso-bidi-font-size: 12.0pt;mso-ansi-language:EN-US;mso-no-proof:yes'>[2]<span style='mso-tab-count: 1'>   </span>G. Melendrez-Caicedo and J. Llerena-Izquierdo,  Secure Data Model for the Healthcare Industry in Ecuador Using Blockchain Technology, <i>Smart Innov. Syst. Technol.</i>, vol. 252, pp. 479 489, 2022, doi: 10.1007/978-981-16-4126-8_43.<o:p></o:p></span></p> <p class=MsoNormal style='margin-left:21.3pt;text-align:justify;text-indent: -21.3pt;mso-layout-grid-align:none'><span style='font-size:10.0pt;mso-bidi-font-size: 12.0pt;mso-ansi-language:EN-US;mso-no-proof:yes'>[3]<span style='mso-tab-count: 1'>   </span>A. Alim and D. Shukla,  A parameter estimation model of big data setup, <i>IDEA</i>, 2020, doi: 10.1109/IDEA49133.2020.9170664.<o:p></o:p></span></p> <p class=MsoNormal style='margin-left:21.3pt;text-align:justify;text-indent: -21.3pt;mso-layout-grid-align:none'><span lang=ES style='font-size:10.0pt; mso-bidi-font-size:12.0pt;mso-no-proof:yes'>[4]<span style='mso-tab-count:1'>   </span>A. de la Nube Toral Sarmiento <i>et al.</i>,  4to. 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Yassine,  Secure confidential big data sharing in cloud computing, in <i>iCBDCA</i>, Mar. 2017, vol. Part F1294, pp. 1 4, doi: 10.1145/3090354.3090388.<o:p></o:p></span></p> <p class=MsoNormal style='margin-left:21.3pt;text-align:justify;text-indent: -21.3pt;mso-layout-grid-align:none'><span style='font-size:10.0pt;mso-bidi-font-size: 12.0pt;mso-ansi-language:EN-US;mso-no-proof:yes'>[45]<span style='mso-tab-count: 1'> </span>L. Kantner and S. D. Goold,  Web tool for health insurance design by small groups, in <i>CHI</i>, Apr. 2006, pp. 141 146, doi: 10.1145/1125451.1125484.</span><span style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-ansi-language:EN-US; mso-no-proof:yes'><o:p></o:p></span></p> <p class=MsoBodyText style='margin-left:21.3pt;text-align:justify;text-indent: -21.3pt'><!--[if supportFields]><span lang=ES><span style='mso-element:field-end'></span></span><![endif]--><span style='mso-ansi-language:EN-US'><o:p>&nbsp;</o:p></span></p> <h1 style='margin-left:21.3pt;text-align:justify;text-indent:-21.3pt'><span style='mso-ansi-language:EN-US'><o:p>&nbsp;</o:p></span></h1> <h1><span style='mso-ansi-language:EN-US'><o:p>&nbsp;</o:p></span></h1> <p class=MsoNormal style='margin-right:-.05pt;text-align:justify'><span style='font-size:10.0pt;mso-ansi-language:EN-US'><o:p>&nbsp;</o:p></span></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><b><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-bidi-font-style:italic'>Nota contribución de los autores:<o:p></o:p></span></b></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-bidi-font-style:italic'>1. Concepción y diseño del estudio<o:p></o:p></span></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-bidi-font-style:italic'>2. Adquisición de datos<o:p></o:p></span></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-bidi-font-style:italic'>3. Análisis de datos<o:p></o:p></span></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-bidi-font-style:italic'>4. Discusión de los resultados<o:p></o:p></span></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-bidi-font-style:italic'>5. Redacción del manuscrito<o:p></o:p></span></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-bidi-font-style:italic'>6. Aprobación de la versión final del manuscrito<o:p></o:p></span></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-bidi-font-style:italic'><o:p>&nbsp;</o:p></span></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-bidi-font-style:italic'>JZP ha contribuido en: 1, 2, 3, 4, 5 y 6.<o:p></o:p></span></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-bidi-font-style:italic'>JLI ha contribuido en: 1, 2, 3, 4, 5 y 6.<o:p></o:p></span></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><i style='mso-bidi-font-style:normal'><span lang=ES style='font-size:10.0pt; mso-bidi-font-size:11.0pt'><o:p>&nbsp;</o:p></span></i></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><i style='mso-bidi-font-style:normal'><span lang=ES style='font-size:10.0pt; mso-bidi-font-size:11.0pt'><o:p>&nbsp;</o:p></span></i></p> <p class=MsoNormal style='margin-top:6.85pt;margin-right:-.05pt;margin-bottom: 0in;margin-left:0in;margin-bottom:.0001pt;text-align:justify'><b><span lang=ES style='font-size:10.0pt;mso-bidi-font-size:11.0pt;mso-bidi-font-style:italic'>Nota de aceptación: </span></b><span lang=ES style='font-size:10.0pt;mso-bidi-font-size: 11.0pt;mso-bidi-font-style:italic'>Este artículo fue aprobado por los editores de la revista Dr. Rafael Sotelo y Mag. Ing. Fernando A. Hernández Gobertti.<b><o:p></o:p></b></span></p> <p class=MsoNormal style='margin-right:-.05pt;text-align:justify'><span style='font-size:10.0pt;mso-ansi-language:EN-US'><o:p>&nbsp;</o:p></span></p> </div> <div style='mso-element:footnote-list'><![if !supportFootnotes]><br clear=all> <hr align=left size=1 width="33%"> <![endif]> <div style='mso-element:footnote' id=ftn1> <p class=MsoNormal><a style='mso-footnote-id:ftn1' href="#_ftnref1" name="_ftn1" title=""><span class=MsoFootnoteReference><span lang=ES style='color:black; mso-themecolor:text1'><span style='mso-special-character:footnote'><![if !supportFootnotes]><span class=MsoFootnoteReference><span lang=ES style='font-size:11.0pt;font-family: "Times New Roman",serif;mso-fareast-font-family:"Times New Roman";color:black; mso-themecolor:text1;mso-ansi-language:ES;mso-fareast-language:ES;mso-bidi-language: ES'>[1]</span></span><![endif]></span></span></span></a><span lang=ES style='color:black;mso-themecolor:text1'> </span><span lang=ES style='font-size:8.0pt;mso-bidi-font-size:11.0pt;color:black;mso-themecolor: text1'>Ingeniero en Sistemas, GIEACI Research Group, </span><span lang=ES><a href="https://gieaci.blog.ups.edu.ec/"><span style='font-size:8.0pt;mso-bidi-font-size: 11.0pt;color:black;mso-themecolor:text1;text-decoration:none;text-underline: none'>https://gieaci.blog.ups.edu.ec/</span></a></span><span lang=ES style='font-size:8.0pt;mso-bidi-font-size:11.0pt;color:black;mso-themecolor: text1'>, </span><span lang=ES><a href="mailto:jzerega@est.ups.edu.ec"><span style='font-size:8.0pt;mso-bidi-font-size:11.0pt;color:black;mso-themecolor: text1;text-decoration:none;text-underline:none'>jzerega@est.ups.edu.ec</span></a></span><span lang=ES style='font-size:8.0pt;mso-bidi-font-size:11.0pt;color:black; mso-themecolor:text1'>, Universidad Politécnica Salesiana, Guayaquil, Ecuador, ORCID iD: </span><span lang=ES><a href="https://orcid.org/0000-0001-7688-5149"><span style='font-size:8.0pt;mso-bidi-font-size:11.0pt;color:black;mso-themecolor: text1;text-decoration:none;text-underline:none'>https://orcid.org/0000-0001-7688-5149</span></a></span><span lang=ES style='font-size:8.0pt;color:black;mso-themecolor:text1'><o:p></o:p></span></p> </div> <div style='mso-element:footnote' id=ftn2> <p class=MsoNormal style='text-align:justify'><a style='mso-footnote-id:ftn2' href="#_ftnref2" name="_ftn2" title=""><span class=MsoFootnoteReference><span lang=ES style='color:black;mso-themecolor:text1'><span style='mso-special-character: footnote'><![if !supportFootnotes]><span class=MsoFootnoteReference><span lang=ES style='font-size:11.0pt;font-family:"Times New Roman",serif;mso-fareast-font-family: "Times New Roman";color:black;mso-themecolor:text1;mso-ansi-language:ES; mso-fareast-language:ES;mso-bidi-language:ES'>[2]</span></span><![endif]></span></span></span></a><span lang=ES style='color:black;mso-themecolor:text1'> </span><span lang=ES style='font-size:8.0pt;mso-bidi-font-size:11.0pt;color:black;mso-themecolor: text1'>Magister en Sistemas de Información Gerencial, GieTICEA Educational Innovation Group, <o:p></o:p></span></p> <p class=MsoNormal style='text-align:justify'><span lang=ES><a href="https://gieaci.blog.ups.edu.ec/"><span style='font-size:8.0pt;mso-bidi-font-size: 11.0pt;color:black;mso-themecolor:text1;text-decoration:none;text-underline: none'>https://gieaci.blog.ups.edu.ec/</span></a></span><span lang=ES style='font-size:8.0pt;mso-bidi-font-size:11.0pt;color:black;mso-themecolor: text1'>, </span><span lang=ES><a href="mailto:jllerena@ups.edu.ec"><span style='font-size:8.0pt;mso-bidi-font-size:11.0pt;color:black;mso-themecolor: text1;text-decoration:none;text-underline:none'>jllerena@ups.edu.ec</span></a></span><span lang=ES style='font-size:8.0pt;mso-bidi-font-size:11.0pt;color:black; mso-themecolor:text1'>, Universidad Politécnica Salesiana, Guayaquil, Ecuador, <o:p></o:p></span></p> <p class=MsoNormal style='text-align:justify'><span lang=ES style='font-size: 8.0pt;mso-bidi-font-size:11.0pt;color:black;mso-themecolor:text1'>ORCID iD: </span><span lang=ES><a href="https://orcid.org/0000-0001-9907-7048"><span style='font-size: 8.0pt;mso-bidi-font-size:11.0pt;color:black;mso-themecolor:text1;text-decoration: none;text-underline:none'>https://orcid.org/0000-0001-9907-7048</span></a></span><span lang=ES style='font-size:8.0pt;color:black;mso-themecolor:text1;mso-fareast-language: EN-US;mso-bidi-language:AR-SA'><o:p></o:p></span></p> </div> </div> </body> </html>