The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the identification of COVID-19’s impact on the mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the changes in the mood state of children and adolescents during the first lockdown. Therefore, in this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best-performing model; and (vi) a post hoc interpretation of the features’ impact on the best-performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of the COVID-19 pandemic on the mood state of children and adolescents.
Language, socio-emotional and cognitive development in children and adolescents with mental health issues is getting increased attention over the last years. Establishing communication patterns and addressing behavioural diversities among this population should be of priority, along with a better understanding in a large variety of patient characteristics within the operational framework of mental healthcare centers. Therefore, the relationships between provided services and operational capability should become more evident. As integrated systems' approaches are still missing to predict the efficiency of treatment services in a macroscopic scale, a General Systems Theory framework is hereby proposed. This framework is applied and tested against the operational framework of the Hellenic Center of Mental Health and Research, in order to identify the need of such an approach and the strong cooperation between medical and population interactions. Using such frameworks as a prerequisite to identify important factors affecting population states can lead to evaluating their impact on the treatment outcome and depict the complexity of pathways potentially related to the children's development.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.