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Title: Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients
Authors: 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
Keywords: COVID-19; OCD; Y-BOCS; classification; machine learning; obsessive-compulsive disorder; regression
Issue Date: 2022
Project: 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. 
Xunta de Galicia under accreditation 2019-2022 ED431G-2019/04 
PTDC/PSIESP/ 29701/2017 
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 
Serial title, monograph or event: Frontiers in Neuroinformatics
Volume: 16
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.
ISSN: 1662-5196
DOI: 10.3389/fninf.2022.807584
Rights: openAccess
Appears in Collections:FPCEUC - Artigos em Revistas Internacionais

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