Dynamic and Baseline Multi-Task Learning for Predicting Substance Use Initiation in the ABCD Study
This study demonstrates that integrating multi-task learning with dynamic, longitudinal modeling significantly improves the prediction of adolescent substance use initiation compared to static baseline approaches, primarily by leveraging time-varying risk factors and shared information across different substances.