Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/103261
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tubío-Fungueiriño, María | - |
dc.contributor.author | Cernadas, Eva | - |
dc.contributor.author | Gonçalves, Óscar | - |
dc.contributor.author | Segalas, Cinto | - |
dc.contributor.author | Bertolín, Sara | - |
dc.contributor.author | Mar-Barrutia, Lorea | - |
dc.contributor.author | Real, Eva | - |
dc.contributor.author | Fernández-Delgado, Manuel | - |
dc.contributor.author | Menchón, Jose M | - |
dc.contributor.author | Carvalho, Sandra | - |
dc.contributor.author | Alonso, Pino | - |
dc.contributor.author | Carracedo, Angel | - |
dc.contributor.author | Fernández-Prieto, Montse | - |
dc.date.accessioned | 2022-10-31T09:42:54Z | - |
dc.date.available | 2022-10-31T09:42:54Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 1662-5196 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/103261 | - |
dc.description.abstract | 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. | pt |
dc.description.sponsorship | Instituto de Salud Carlos III (COV20_00622) and cofunded by European Union (ERDF) “A way of making Europe” and Covid funds of Fundación Amancio Ortega and Banco de Santander. Carlos III Health Institute (PI16/00950, PI18/00856) and FEDER funds (“A way to build Europe”), as well as CERCA Programme/Generalitat de Catalunya for institutional support. | pt |
dc.language.iso | eng | pt |
dc.relation | PTDC/PSIESP/ 29701/2017 | pt |
dc.relation | Xunta de Galicia | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | COVID-19 | pt |
dc.subject | OCD | pt |
dc.subject | Y-BOCS | pt |
dc.subject | classification | pt |
dc.subject | machine learning | pt |
dc.subject | obsessive-compulsive disorder | pt |
dc.subject | regression | pt |
dc.title | Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients | pt |
dc.type | article | - |
degois.publication.firstPage | 807584 | pt |
degois.publication.title | Frontiers in Neuroinformatics | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.3389/fninf.2022.807584 | pt |
degois.publication.volume | 16 | pt |
dc.date.embargo | 2022-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.fulltext | Com Texto completo | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.researchunit | Center for Research in Neuropsychology and Cognitive Behavioral Intervention (CINEICC) | - |
crisitem.author.parentresearchunit | Faculty of Psychology and Educational Sciences | - |
crisitem.author.orcid | 0000-0003-2735-9155 | - |
Appears in Collections: | FPCEUC - Artigos em Revistas Internacionais |
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File | Description | Size | Format | |
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fninf-16-807584.pdf | 1.17 MB | Adobe PDF | View/Open |
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