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.

Efficacy of Low-Dose Aripiprazole for Treatment of Psychotic Symptoms in a Patient with 22q11.2 Deletion Syndrome.

Objectives: The 22q11.2 deletion syndrome is one of the most prevalent genetic disorders and children suffering from this syndrome have been found to have a substantially greater risk for the development of schizophrenia and bipolar disorder. Psychiatric medications such as antipsychotics are commonly used in individuals with 22q11.2DS. Experimental Design: This is a case of 13 years male young man suffering from 22q11.2 deletion syndrome. Since adolescence, he presented with behavioural disorders, aggression, verbal abuse, sleep disorders. Principal Observations: The psychiatric examination confirmed the delusional idea, which was repeated in an obsessive way. There were also auditory hallucinations along with reference ideas. Aripiprazole was administered in 8 mg daily which gave more spectacular results and was better tolerated. Conclusions: In the present situation delusional ideas are no longer mentioned, but a cognitive deduction is found. Aripiprazole can be an effective pharmacological solution for the psychotic symptoms in patients suffering from 22q11DS.