Leveraging Machine Learning to Identify Predictors of Receiving Psychosocial Treatment for Attention Deficit/Hyperactivity Disorder.

This study aimed to identify factors associated with receiving psychosocial treatment for ADHD in a nationally representative sample. Participants were 6630 youth with a parent-reported diagnosis of ADHD from the 2016-2017 National Survey of Children's Health. Machine learning analyses were performed to identify factors associated with receipt of psychosocial treatment for ADHD. We examined potentially associated factors in the broad categories of variables hypothesized to affect problem recognition (e.g., severity, mental health comorbidities); the decision to seek treatment; service selection (e.g., insurance coverage) and service use. We found that three machine learning models unanimously identified parent-reported ADHD severity (mild vs. moderate/severe) as the factor that best distinguishes between children who receive psychosocial treatment for ADHD and those who do not. Receive operating characteristic curve analysis revealed the following model performance: classification and regression tree analysis (area under the curve; AUC = .68); an ensemble model (AUC = .71); and a deep, multi-layer neural network (AUC = .72), as well as comparison to a logistic regression model (AUC = .69). Further, insurance coverage of mental/behavioral health needs emerged as a salient factor associated with the receipt of psychosocial treatment. Machine learning models identified risk and protective factors that predicted the receipt of psychosocial treatment for ADHD, such as ADHD severity and health insurance coverage.