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Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning
During the COVID-19 pandemic, an increase in the incidence of psychiatric disorders in the general population and an increase in the severity of symptoms in psychiatric patients have been reported. Anxiety and depression symptoms are the most commonly observed during large-scale dramatic events such...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025309/ https://www.ncbi.nlm.nih.gov/pubmed/35454005 http://dx.doi.org/10.3390/diagnostics12040957 |
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author | D’Urso, Giordano Magliacano, Alfonso Rotbei, Sayna Iasevoli, Felice de Bartolomeis, Andrea Botta, Alessio |
author_facet | D’Urso, Giordano Magliacano, Alfonso Rotbei, Sayna Iasevoli, Felice de Bartolomeis, Andrea Botta, Alessio |
author_sort | D’Urso, Giordano |
collection | PubMed |
description | During the COVID-19 pandemic, an increase in the incidence of psychiatric disorders in the general population and an increase in the severity of symptoms in psychiatric patients have been reported. Anxiety and depression symptoms are the most commonly observed during large-scale dramatic events such as pandemics and wars, especially when these implicate an extended lockdown. The early detection of higher risk clinical and non-clinical individuals would help prevent the new onset and/or deterioration of these symptoms. This in turn would lead to the implementation of public policies aimed at protecting vulnerable populations during these dramatic contingencies, therefore optimising the effectiveness of interventions and saving the resources of national healthcare systems. We used a supervised machine learning method to identify the predictors of the severity of psychiatric symptoms during the Italian lockdown due to the COVID-19 pandemic. Via a case study, we applied this methodology to a small sample of healthy individuals, obsessive-compulsive disorder patients, and adjustment disorder patients. Our preliminary results show that our models were able to predict depression, anxiety, and obsessive-compulsive symptoms during the lockdown with up to 92% accuracy based on demographic and clinical characteristics collected before the pandemic. The presented methodology may be used to predict the psychiatric prognosis of individuals under a large-scale lockdown and thus supporting the related clinical decisions. |
format | Online Article Text |
id | pubmed-9025309 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90253092022-04-23 Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning D’Urso, Giordano Magliacano, Alfonso Rotbei, Sayna Iasevoli, Felice de Bartolomeis, Andrea Botta, Alessio Diagnostics (Basel) Article During the COVID-19 pandemic, an increase in the incidence of psychiatric disorders in the general population and an increase in the severity of symptoms in psychiatric patients have been reported. Anxiety and depression symptoms are the most commonly observed during large-scale dramatic events such as pandemics and wars, especially when these implicate an extended lockdown. The early detection of higher risk clinical and non-clinical individuals would help prevent the new onset and/or deterioration of these symptoms. This in turn would lead to the implementation of public policies aimed at protecting vulnerable populations during these dramatic contingencies, therefore optimising the effectiveness of interventions and saving the resources of national healthcare systems. We used a supervised machine learning method to identify the predictors of the severity of psychiatric symptoms during the Italian lockdown due to the COVID-19 pandemic. Via a case study, we applied this methodology to a small sample of healthy individuals, obsessive-compulsive disorder patients, and adjustment disorder patients. Our preliminary results show that our models were able to predict depression, anxiety, and obsessive-compulsive symptoms during the lockdown with up to 92% accuracy based on demographic and clinical characteristics collected before the pandemic. The presented methodology may be used to predict the psychiatric prognosis of individuals under a large-scale lockdown and thus supporting the related clinical decisions. MDPI 2022-04-12 /pmc/articles/PMC9025309/ /pubmed/35454005 http://dx.doi.org/10.3390/diagnostics12040957 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article D’Urso, Giordano Magliacano, Alfonso Rotbei, Sayna Iasevoli, Felice de Bartolomeis, Andrea Botta, Alessio Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning |
title | Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning |
title_full | Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning |
title_fullStr | Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning |
title_full_unstemmed | Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning |
title_short | Predicting the Severity of Lockdown-Induced Psychiatric Symptoms with Machine Learning |
title_sort | predicting the severity of lockdown-induced psychiatric symptoms with machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025309/ https://www.ncbi.nlm.nih.gov/pubmed/35454005 http://dx.doi.org/10.3390/diagnostics12040957 |
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