Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study

A study using data from approximately 11,860 participants in the Adolescent Brain Cognitive Development (ABCD) Study found that longitudinal models outperform baseline models in predicting adolescent substance use onset, with temporal XGBoost achieving the strongest standalone performance. Combining temporal XGBoost and Temporal Graph Convolutional Networks (T-GCNs) through score-level stacking yielded the best performance across all outcomes, achieving AUC-ROC values above 0.79. The findings highlight the value of longitudinal modeling and graph-based representations for substance-use prediction.

arXiv:2608.14578v1 Announce Type: new Abstract: Early identification of adolescent substance-use risk is an important prevention challenge, yet the relative value of baseline characteristics, longitudinal trajectories, and relational context remains unclear. Using data from approximately 11,860 participants in the Adolescent Brain Cognitive Development ABCD Study, we compare cross-sectional, longitudinal, and graph-based approaches for predicting alcohol sipping, alcohol use, marijuana use, and alcohol/marijuana use. We evaluate tree-based models, recurrent neural networks, and Temporal Graph Convolutional Networks T-GCNs constructed from family, school, and feature-similarity graphs. Longitudinal models consistently outperform baseline models, with temporal XGBoost achieving the strongest standalone performance. Although T-GCNs generally do not surpass temporal XGBoost, graph-derived risk scores provide complementary information. Combining temporal XGBoost and T-GCN predictions through score-level stacking yields the best performance across all outcomes, achieving AUC-ROC values above 0.79. Feature analyses identify peer deviance, age, externalizing symptoms, parental monitoring, cultural norms, and neighborhood context as important predictors of substance use onset. These findings demonstrate the value of longitudinal modeling for substance-use prediction and suggest that graph-based representations can provide effective auxiliary risk signals.