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

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

Parental Stress Scale: Psychometric Properties in Parents of Preschool Children with Developmental Language Disorder

Parents of children with developmental disabilities experience more stress compared to those of typically developing children; therefore, measuring parental stress may help clinicians to address it. The Parental Stress Scale (PSS) is a self-rceport measure in the public domain that assesses stress related to child rearing. The present study tested the psychometric properties of the Greek version of the PSS in 204 parents (mean age: 39.4 ± 5.7, 124 mothers and 80 fathers) of kindergarten children diagnosed with Developmental Language Disorder (DLD) after a clinical assessment. Confirmatory factor analysis (CFA) was used to confirm the original four-factor structure. The results showed that the original four-factor structure (parental rewards, parental stressors, lack of control and parental satisfaction) is valid in this specific Greek population. The reliability was high (ω = 0.78) and there were weak correlations (r = −0.372, r = −0.337, r = −0.236), yet of statistical significance (p < 0.001), with similar psychological constructs (quality of life, emotional functioning and worries). Our data confirmed that the PSS is a reliable and valid tool to measure parental stress in parents of children with DLD. Greek clinicians (mental health professionals, speech-language pathologists) can evaluate parental stress and design early interventions targeting specific stress aspects, along with core language interventions for the children

Health-Related Quality of Life and Behavioral Difficulties in Greek Preschool Children with Developmental Language Disorder

Developmental language disorder (DLD) has a great impact on language skills as well as on a wide range of functioning areas, such as social and school functioning. In the present study, we aim to explore the Health-Related Quality of Life (HRQoL) of preschool children with DLD, compared to children with no language difficulties, using a self and proxy report method. A total of 230 parents of preschool children with DLD and 146 parents of children without language difficulties completed the Pediatric Quality of Life Inventory (PedsQLTM) 4.0 Generic Core Module and the Strengths and Difficulties Questionnaire (SDQ). Additionally, 71 children with DLD and 55 peers without DLD completed the self-reported PedsQLTM module. The parents of kindergarten children (5–6 years old) with DLD reported that their kids experience worse social and school functioning compared to the control group. In addition, the children with DLD self-reported lower physical and social functioning. The parents of children with DLD reported that their children experience higher hyperactivity/inattention problems than the parents of the control group. Kindergarten children with DLD have a poorer HRQoL compared to their peers, as perceived by themselves and their parents. Moreover, children with DLD present with higher hyperactivity and inattention symptoms. Health professionals working with children who have DLD need to consider not only the language difficulties but also the children’s wellbeing and symptoms of hyperactivity and inattention

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

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

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

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. <i>Healthcare</i> 2022, <i>10</i>, 149

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