Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study
Harnessing T-GCNs and longitudinal modeling to decode adolescent risk factors in the ABCD study.
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 B...
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Integrating longitudinal data with graph-based relational insights significantly improves the precision of early substance-use risk assessments. This hybrid modeling approach provides a more holistic view of adolescent behavioral trajectories by capturing both individual developmental patterns and complex environmental influences.
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