Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients

Detalhes bibliográficos
Autor(a) principal: Tubío-Fungueiriño, María
Data de Publicação: 2022
Outros Autores: Cernadas, Eva, Gonçalves, Óscar, Segalas, Cinto, Bertolín, Sara, Mar-Barrutia, Lorea, Real, Eva, Fernández-Delgado, Manuel, Menchón, Jose M, Carvalho, Sandra, Alonso, Pino, Carracedo, Angel, Fernández-Prieto, Montse
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/103261
https://doi.org/10.3389/fninf.2022.807584
Resumo: Machine learning modeling can provide valuable support in different areas of mental health, because it enables to make rapid predictions and therefore support the decision making, based on valuable data. However, few studies have applied this method to predict symptoms' worsening, based on sociodemographic, contextual, and clinical data. Thus, we applied machine learning techniques to identify predictors of symptomatologic changes in a Spanish cohort of OCD patients during the initial phase of the COVID-19 pandemic.
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spelling Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder PatientsCOVID-19OCDY-BOCSclassificationmachine learningobsessive-compulsive disorderregressionMachine learning modeling can provide valuable support in different areas of mental health, because it enables to make rapid predictions and therefore support the decision making, based on valuable data. However, few studies have applied this method to predict symptoms' worsening, based on sociodemographic, contextual, and clinical data. Thus, we applied machine learning techniques to identify predictors of symptomatologic changes in a Spanish cohort of OCD patients during the initial phase of the COVID-19 pandemic.2022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/103261http://hdl.handle.net/10316/103261https://doi.org/10.3389/fninf.2022.807584eng1662-5196Tubío-Fungueiriño, MaríaCernadas, EvaGonçalves, ÓscarSegalas, CintoBertolín, SaraMar-Barrutia, LoreaReal, EvaFernández-Delgado, ManuelMenchón, Jose MCarvalho, SandraAlonso, PinoCarracedo, AngelFernández-Prieto, Montseinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-10-31T21:32:36Zoai:estudogeral.uc.pt:10316/103261Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:20:07.474217Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients
title Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients
spellingShingle Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients
Tubío-Fungueiriño, María
COVID-19
OCD
Y-BOCS
classification
machine learning
obsessive-compulsive disorder
regression
title_short Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients
title_full Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients
title_fullStr Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients
title_full_unstemmed Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients
title_sort Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients
author Tubío-Fungueiriño, María
author_facet Tubío-Fungueiriño, María
Cernadas, Eva
Gonçalves, Óscar
Segalas, Cinto
Bertolín, Sara
Mar-Barrutia, Lorea
Real, Eva
Fernández-Delgado, Manuel
Menchón, Jose M
Carvalho, Sandra
Alonso, Pino
Carracedo, Angel
Fernández-Prieto, Montse
author_role author
author2 Cernadas, Eva
Gonçalves, Óscar
Segalas, Cinto
Bertolín, Sara
Mar-Barrutia, Lorea
Real, Eva
Fernández-Delgado, Manuel
Menchón, Jose M
Carvalho, Sandra
Alonso, Pino
Carracedo, Angel
Fernández-Prieto, Montse
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Tubío-Fungueiriño, María
Cernadas, Eva
Gonçalves, Óscar
Segalas, Cinto
Bertolín, Sara
Mar-Barrutia, Lorea
Real, Eva
Fernández-Delgado, Manuel
Menchón, Jose M
Carvalho, Sandra
Alonso, Pino
Carracedo, Angel
Fernández-Prieto, Montse
dc.subject.por.fl_str_mv COVID-19
OCD
Y-BOCS
classification
machine learning
obsessive-compulsive disorder
regression
topic COVID-19
OCD
Y-BOCS
classification
machine learning
obsessive-compulsive disorder
regression
description Machine learning modeling can provide valuable support in different areas of mental health, because it enables to make rapid predictions and therefore support the decision making, based on valuable data. However, few studies have applied this method to predict symptoms' worsening, based on sociodemographic, contextual, and clinical data. Thus, we applied machine learning techniques to identify predictors of symptomatologic changes in a Spanish cohort of OCD patients during the initial phase of the COVID-19 pandemic.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/103261
http://hdl.handle.net/10316/103261
https://doi.org/10.3389/fninf.2022.807584
url http://hdl.handle.net/10316/103261
https://doi.org/10.3389/fninf.2022.807584
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1662-5196
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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