Correction: Ntakolia et al. An Explainable Machine Learning Approach for COVID-19's Impact on Mood States of Children and Adolescents during the First Lockdown in Greece. Healthcare 2022, 10, 149.

Argyris Stringaris was initially included as an author in the original publication [...].

An Explainable Machine Learning Approach for COVID-19's Impact on Mood States of Children and Adolescents during the First Lockdown in Greece.

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.

A call for prevention and early intervention in obsessive-compulsive disorder.

BACKGROUND: Evidence suggests that many people with obsessive-compulsive disorder (OCD) have subclinical symptoms years before the development of their disorder and that early treatment may reduce its severity. AIM: To explore prevention and early intervention strategies for OCD. METHODS: A narrative literature review was conducted. RESULTS: The literature in relation to the prevention of OCD is sparse. Genetic and environmental factors appear to be relevant to the aetiology of OCD, for example, the observation that hoarding symptoms and contamination/cleaning symptoms are more likely to also be present in first-degree relatives. Psychoeducation and the reduction of family accommodation, that is the act of parents, siblings or partners accommodating to the high-risk individual's requests to comply with their compulsions, are promising areas for prevention and early intervention in high-risk groups. Tertiary prevention has also been limited by an inadequate number of trained clinicians to deliver evidence-based treatments. CONCLUSIONS: Much more research is needed in relation to the prevention of OCD. There is limited scope for primary prevention with respect to biological aetiological factors, but there is potential for strategies addressing environmental factors (eg, family factors). The effectiveness of psychoeducation for parents with OCD as a primary prevention strategy for OCD in their children requires scientific evaluation. Improving access to effective treatments for OCD would also improve tertiary prevention.