Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/103261
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: | PTDC/PSIESP/ 29701/2017 Xunta de Galicia |
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. | URI: | https://hdl.handle.net/10316/103261 | ISSN: | 1662-5196 | DOI: | 10.3389/fninf.2022.807584 | Rights: | openAccess |
Appears in Collections: | FPCEUC - Artigos em Revistas Internacionais |
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fninf-16-807584.pdf | 1.17 MB | Adobe PDF | View/Open |
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