Mostrar o rexistro simple do ítem

dc.contributor.authorCacheda, Fidel
dc.contributor.authorFernández, Diego
dc.contributor.authorNóvoa, Francisco
dc.contributor.authorCarneiro, Víctor
dc.date.accessioned2019-07-01T15:40:13Z
dc.date.available2019-07-01T15:40:13Z
dc.date.issued2019-06-10
dc.identifier.citationCacheda F, Fernandez D, Novoa FJ, Carneiro V Early Detection of Depression: Social Network Analysis and Random Forest Techniques J Med Internet Res 2019;21(6):e12554 URL: https://www.jmir.org/2019/6/e12554 DOI: 10.2196/12554 PMID: 31199323es_ES
dc.identifier.issn1438-8871
dc.identifier.otherPMID: 31199323
dc.identifier.urihttp://hdl.handle.net/2183/23328
dc.description.abstract[Abstract] Background: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder. Objective: This study used data from social media networks to explore various methods of early detection of MDDs based on machine learning. We performed a thorough analysis of the dataset to characterize the subjects’ behavior based on different aspects of their writings: textual spreading, time gap, and time span. Methods: We proposed 2 different approaches based on machine learning singleton and dual. The former uses 1 random forest (RF) classifier with 2 threshold functions, whereas the latter uses 2 independent RF classifiers, one to detect depressed subjects and another to identify nondepressed individuals. In both cases, features are defined from textual, semantic, and writing similarities. Results: The evaluation follows a time-aware approach that rewards early detections and penalizes late detections. The results show how a dual model performs significantly better than the singleton model and is able to improve current state-of-the-art detection models by more than 10%. Conclusions: Given the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks.es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; TIN2015-70648-Pes_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01 2016-2019es_ES
dc.language.isoenges_ES
dc.publisherJ M I R Publications, Inc.es_ES
dc.relation.urihttps://www.jmir.org/2019/6/e12554/es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectDepressiones_ES
dc.subjectMajor depressive disorderes_ES
dc.subjectSocial mediaes_ES
dc.subjectArtificial intelligencees_ES
dc.subjectMachine learninges_ES
dc.titleEarly Detection of Depression: Social Network Analysis and Random Forest Techniqueses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleJournal of Medical Internet Researches_ES
UDC.volume21es_ES
UDC.issue6es_ES
dc.identifier.doi10.2196/12554


Ficheiros no ítem

Thumbnail
Thumbnail

Este ítem aparece na(s) seguinte(s) colección(s)

Mostrar o rexistro simple do ítem