EHR-based machine learning for psychiatric outcomes in youth and young adults: Challenges and opportunities

Reducing mental health struggles in adolescents and young adults is a clinical and public health priority, given the prevalence of neuropsychiatric symptoms and their potential to disrupt key life transitions and the foundation for later independence. Yet, effective early identification and support of vulnerable youth require improved stratification of risk for psychopathology and associated psychosocial difficulties. In adults, the application of machine learning (ML) methods to large-scale electronic health record (EHR) data has shown some promise for improving prediction of psychiatric outcomes. In this focused review, we discuss the small but growing body of work applying ML to EHR data across childhood and adolescence in the context of this adult literature. In doing so, we highlight efforts to augment structured EHR data with dimensional and clinically-relevant constructs based on natural language processing (NLP) and patient-reported outcomes. Finally, we note ongoing initiatives and priorities for advancing the evidence base to promote clinically actionable and developmentally informed risk stratification in youth.