Explainable AI-based identification of contributing factors to the mood state change in children and adolescents with pre-existing psychiatric disorders in the context of COVID-19-related lockdowns in Greece

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Ntakolia, Charis, Priftis, Dimitrios, Kotsis, Konstantinos, Magklara, Konstantina, Charakopoulou-Travlou, Mariana, Rannou, Ioanna, Ladopoulou, Konstantina, Koullourou, Iouliani, Tsalamanios, Emmanouil, Lazaratou, Eleni, Serdari, Aspasia, Grigoriadou, Aliki, Sadeghi, Neda, Chiu, Kenny ORCID: https://orcid.org/0000-0001-8776-9864 and Giannopoulou, Ioanna (2023) Explainable AI-based identification of contributing factors to the mood state change in children and adolescents with pre-existing psychiatric disorders in the context of COVID-19-related lockdowns in Greece. BioMedInformatics, 3 (4). pp. 1040-1059. ISSN 2673-7426

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Abstract

The COVID-19 pandemic and its accompanying restrictions have significantly impacted people’s lives globally. There is an increasing interest in examining the influence of this unprecedented situation on our mental well-being, with less attention towards the impact of the elongation of COVID-19-related measures on youth with a pre-existing psychiatric/developmental disorder. The majority of studies focus on individuals, such as students, adults, and youths, among others, with little attention being given to the elongation of COVID-19-related measures and their impact on a special group of individuals, such as children and adolescents with diagnosed developmental and psychiatric disorders. In addition, most of these studies adopt statistical methodologies to identify pair-wise relationships among factors, an approach that limits the ability to understand and interpret the impact of various factors. In response, this study aims to adopt an explainable machine learning approach to identify factors that explain the deterioration or amelioration of mood state in a youth clinical sample. The purpose of this study is to identify and interpret the impact of the greatest contributing features of mood state changes on the prediction output via an explainable machine learning pipeline. Among all the machine learning classifiers, the Random Forest model achieved the highest effectiveness, with 76% best AUC-ROC Score and 13 features. The explainability analysis showed that stress or positive changes derived from the imposing restrictions and COVID-19 pandemic are the top two factors that could affect mood state.

Item Type:	Article
Uncontrolled Keywords:	covid-19 pandemic,children and adolescents,explainability,machine learning,mental health,computer science (miscellaneous),medicine (miscellaneous),health informatics,health professions (miscellaneous) ,/dk/atira/pure/subjectarea/asjc/1700/1701
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
Related URLs:	http://www.scopus.com/inward/record.url?... https://www.mdpi.com/2673-7426/3/4/62
Depositing User:	LivePure Connector
Date Deposited:	13 Nov 2023 18:01
Last Modified:	24 Sep 2024 12:54
URI:	https://ueaeprints.uea.ac.uk/id/eprint/93630
DOI:	10.3390/biomedinformatics3040062

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