Toward population screening for early signs of mental illness in youth using explainable machine learning

Oregon Convention Center 

Data from population-based surveys can be used to inform public policy related to mental health, but insights derived from these data are often limited as the underlying research tends to focus on only one or a few variables at a time. Here, we demonstrate the utility of explainable machine learning approaches when applied to survey data from school-aged youth in Canada and the United States. First, we accessed data for 11,000 participants (ages 10-11) from the Adolescent Brain and Cognitive Development (ABCD) Study. Using gradient-boosted trees paired with SHapely Additive exPlanations (SHAP), we ranked over 50 bio-psycho-social factors by their importance in explaining variability in problematic behaviors and symptoms. Second, we applied the same methodology to over 20,000 participants (ages 13-18) from the 2019 and 2023 cycles of the Ontario Student Drug Use and Health Survey (OSDUHS). Our goal is to rank and characterize the associations of a broad set of "Whole Person" factors with mental wellbeing in youth. This information can be used by a range of stakeholders, including scientists, policymakers, and the general public, to prioritize areas of focus moving ahead.