ADHD and ODD Dimensions: Time Varying Prediction of Internalizing Problems from Childhood to Adolescence

Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders

Introduction

Fetal alcohol spectrum disorders include a variety of physical and neurocognitive disorders caused by prenatal alcohol exposure. Although their overall prevalence is around 0.77%, FASD remains underdiagnosed and little known, partly due to the complexity of their diagnosis, which shares some symptoms with other pathologies such as autism spectrum, depression or hyperactivity disorders.

Methods

This study included 73 control and 158 patients diagnosed with FASD. Variables selected were based on IOM classification from 2016, including sociodemographic, clinical, and psychological characteristics. Statistical analysis included Kruskal-Wallis test for quantitative factors, Chi-square test for qualitative variables, and Machine Learning (ML) algorithms for predictions.

Results

This study explores the application ML in diagnosing FASD and its subtypes: Fetal Alcohol Syndrome (FAS), partial FAS (pFAS), and Alcohol-Related Neurodevelopmental Disorder (ARND). ML constructed a profile for FASD based on socio-demographic, clinical, and psychological data from children with FASD compared to a control group. Random Forest (RF) model was the most efficient for predicting FASD, achieving the highest metrics in accuracy (0.92), precision (0.96), sensitivity (0.92), F1 Score (0.94), specificity (0.92), and AUC (0.92). For FAS, XGBoost model obtained the highest accuracy (0.94), precision (0.91), sensitivity (0.91), F1 Score (0.91), specificity (0.96), and AUC (0.93). In the case of pFAS, RF model showed its effectiveness, with high levels of accuracy (0.90), precision (0.86), sensitivity (0.96), F1 Score (0.91), specificity (0.83), and AUC (0.90). For ARND, RF model obtained the best levels of accuracy (0.87), precision (0.76), sensitivity (0.93), F1 Score (0.84), specificity (0.83), and AUC (0.88). Our study identified key variables for efficient FASD screening, including traditional clinical characteristics like maternal alcohol consumption, lip-philtrum, microcephaly, height and weight impairment, as well as neuropsychological variables such as the Working Memory Index (WMI), aggressive behavior, IQ, somatic complaints, and depressive problems.

Discussion

Our findings emphasize the importance of ML analyses for early diagnoses of FASD, allowing a better understanding of FASD subtypes to potentially improve clinical practice and avoid misdiagnosis.

Can't Stop, Won't Stop? The Role of Inhibitory Control and Callous-Unemotional Traits in Childhood Conduct Problems and Aggression

Disruptive behaviors such as conduct problems and aggression are some of the most prevalent childhood psychological concerns. The etiology of disruptive behaviors is heterogenous and the relationships between the myriad risk factors that contribute to these problems are not yet fully understood. This study examined the relationship between inhibitory control and callous-unemotional traits (CU traits) with conduct problems and aggression in a community sample of children (aged 6 to 11 years). Caregivers (n= 148) completed a survey assessing a range of known risk factors (including hyperactivity and inattention). Children were found to display more conduct problems and aggression if they had greater difficulties with inhibitory control and a higher number of CU traits. Interestingly, when children had CU traits, inhibitory control difficulties exacerbated the severity of conduct problems (but not aggression). Differences in severity between conduct problems and aggression highlight the unique relationships between risk factors such as inhibitory control and CU traits, and lay the groundwork for future studies to explore the trajectories of this relationship.