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Machine learning aplicado a la predicción y clasificación de la depresión, un enfoque hacia la gestión de la salud mental

dc.contributor.advisorGiraldo Marín, Liliana María
dc.contributor.advisorJaramillo Villegas, Herman Horacio
dc.contributor.authorOsorio Castrillón, Sebastián
dc.coverage.spatialLat: 06 15 00 N  degrees minutes  Lat: 6.2500  decimal degreesLong: 075 36 00 W  degrees minutes  Long: -75.6000  decimal degrees
dc.date2022-10-05
dc.date.accessioned2022-11-15T19:20:38Z
dc.date.available2022-11-15T19:20:38Z
dc.identifier.otherT 0281 2022
dc.identifier.urihttp://hdl.handle.net/11407/7637
dc.descriptionEl principal reto de las aseguradoras de la salud es pasar de una gestión reactiva a una gestión proactiva de sus afiliados, es decir, priorizar la prevención e implementación de acciones que favorezcan la salud en sus dimensiones tanto físicas como mentales y sociales, y no solo que mitiguen la enfermedad. Este artículo presenta una revisión sistemática de la literatura sobre las principales metodologías de aprendizaje automático que permiten a través de la predicción de enfermedades mentales, realizar una intervención temprana. Se encontró que las principales metodologías para dicho fin son los modelos estadísticos como la regresión logística y las redes neuronales; y que los diferentes indicadores de las neuroimágenes y el comportamiento en redes sociales se convierten en variables predictoras fundamentales a la hora de predecir las enfermedades mentales.spa
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.eng
dc.format.extentp. 1-69
dc.format.mediumElectrónico
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0*
dc.subjectAprendizaje automáticospa
dc.subjectMinería de datosspa
dc.subjectPredicción enfermedades mentalesspa
dc.subjectsystematic revieweng
dc.subjectMachine learningeng
dc.subjectDepressioneng
dc.titleMachine learning aplicado a la predicción y clasificación de la depresión, un enfoque hacia la gestión de la salud mentalspa
dc.typeinfo:eu-repo/semantics/masterThesis
dc.rights.accessrightsinfo:eurepo/semantics/openAccess
dc.publisher.programMaestría en Modelación y Ciencia computacionalspa
dc.subject.lembAprendizaje automático (Inteligencia artificial)spa
dc.subject.lembCompañías aseguradorasspa
dc.subject.lembDepresión mental - Métodos estadísticosspa
dc.subject.lembEstadísticas médicasspa
dc.subject.lembMinería de datosspa
dc.subject.lembPredicción (Psicología)spa
dc.subject.lembSalud mentalspa
dc.subject.lembServicios de salud mentalspa
dc.relation.citationstartpage1
dc.relation.citationendpage69
dc.audienceComunidad Universidad de Medellínspa
dc.publisher.facultyFacultad de Ciencias Básicasspa
dc.publisher.placeMedellínspa]
dc.type.hasversionpublishedVersion
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
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dc.rights.creativecommonsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.type.localTesis de Maestríaspa
dc.description.degreenameMagíster en Modelación y Ciencia computacionalspa
dc.description.degreelevelMaestríaspa
dc.publisher.grantorUniversidad de Medellínspa


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