Explainable Ai-Based Identification of Contributing Factors to the Mood State Change of Children and Adolescents with Pre-Existing Psychiatric Disorders in the Context of COVID-19 Related Lockdowns in Greece
18 Pages Posted: 10 Aug 2022
The COVID-19 pandemic and accompanying restrictions have significantly impacted 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 elongation of COVID-19 related measures on youth with a pre-existing psychiatric/developmental disorder. The majority of studies are focusing on individuals, such as students, adults, youths, among others, with little attention to be given to the elongation of COVID-19 related measures and their impact to 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 youth clinical sample. The purpose of this study is to identify and interpret the impact of the most contributing features of mood states change to the prediction output, via an explainable machine learning pipeline. Among all the machine learning classifiers, Random Forest model achieved the highest accuracy, with 76% Best AUC-ROC Score and 13 features. 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.
Funding Information: None.
Declaration of Interests: None.
Ethics Approval Statement: Institutional Review Board Statement: Each CAMHS contacted the parents of all children and adolescents who attended the service from 1 March 2019 to 1 March 2020 and from 16 December 2020 to 8 February 2021. All parents interested in taking part in the survey were sent an email containing information about the study, along with a unique identification code number and the link to log into Google Forms Survey app. After reading the information about the goals of the study, the process of data collection and confidentiality, and providing informed consent online, they proceeded to answer the questionnaire. The study was approved by the Ethics Committee of each hospital, with which the service is affiliated. The study was performed in line with the principles of the Declaration of Helsinki. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Keywords: COVID-19 pandemic, mental health, Machine Learning, explainability, children and adolescents
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