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AI models predict adolescent substance use with longitudinal and graph data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed advanced models to predict the onset of substance use in adolescents using data from the ABCD Study. By comparing cross-sectional, longitudinal, and graph-based approaches, they found that longitudinal models consistently outperformed baseline predictions. Temporal XGBoost showed the strongest standalone performance, while Temporal Graph Convolutional Networks (T-GCNs) offered complementary risk signals. Combining these methods through score-level stacking achieved the best results, identifying key predictors such as peer deviance, age, and parental monitoring. AI

IMPACT Demonstrates advanced AI techniques for early identification of adolescent substance use risk, potentially aiding prevention efforts.

RANK_REASON Academic paper detailing a new predictive modeling approach for adolescent substance use. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI models predict adolescent substance use with longitudinal and graph data

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Academic paper detailing a new predictive modeling approach for adolescent substance use. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yixuan He, Jinni Su, Yun Kang ·

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

    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 appro…

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