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Statistics applied to the prediction of mental illness: A systematic review [Estadística aplicada a la predicción de enfermedades mentales: Una revisión sistemática]

dc.contributor.authorCastrillon S.O
dc.contributor.authorMarín L.M.G
dc.contributor.authorVillegas H.H.J
dc.contributor.authorEscobar C.C.P.
dc.date.accessioned2022-09-14T14:34:09Z
dc.date.available2022-09-14T14:34:09Z
dc.date.created2021
dc.identifier.issn16469895
dc.identifier.urihttp://hdl.handle.net/11407/7581
dc.descriptionThe main challenge for health insurers is to move from reactive to proactive management of their clients, that is, prioritize prevention and implementation of actions that favor health in its physical, mental, and social dimensions, and not only that mitigate the disease. This article presents a systematic review of the literature on the main machine learning methodologies that allow, through the prediction of mental illnesses, to carry out an early intervention. It was found that the main methodologies for this purpose are statistical models such as logistic regression and neural networks; and that the different indicators of neuroimaging and behavior in social networks become fundamental predictor variables when it comes to predicting mental illnesses. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.eng
dc.language.isospa
dc.publisherAssociacao Iberica de Sistemas e Tecnologias de Informacao
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85124384761&partnerID=40&md5=b5822bcdb03892a342dc99c109469497
dc.sourceRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
dc.titleStatistics applied to the prediction of mental illness: A systematic review [Estadística aplicada a la predicción de enfermedades mentales: Una revisión sistemática]
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Sistemas
dc.publisher.programCiencias Básicas
dc.type.spaArtículo
dc.subject.keywordData mining-Mentall Illness-predictioneng
dc.subject.keywordMachine learningeng
dc.relation.citationissueE43
dc.relation.citationstartpage266
dc.relation.citationendpage275
dc.publisher.facultyFacultad de Ingenierías
dc.publisher.facultyFacultad de Ciencias Básicas
dc.affiliationCastrillon, S.O., Estudiante Maestría en Modelación y Ciencia Computacional, Universidad de Medellín, Antioquia, Colombia
dc.affiliationMarín, L.M.G., Facultad de Ingenierías, Universidad de Medellín, Antioquia, Colombia
dc.affiliationVillegas, H.H.J., Facultad de Ciencias Básicas, Universidad de Medellín, Antioquia, Colombia
dc.affiliationEscobar, C.C.P., Facultad de Ciencias Básicas, Universidad de Medellín, Antioquia, Colombia
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dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/article
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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