A Machine Learning Model Can Predict Which Young Cannabis Users Will Develop Use Disorder

The model achieved AUC values of 0.68 (training), 0.64, and 0.75 (two validation datasets) for predicting CUD within 5 years of first cannabis use. The five risk factors were biological sex, delinquency, and personality traits of conscientiousness, neuroticism, and openness. Calibration was excellent (E/O ratios of 0.95–1.0).

Bayesian machine learning model trained on the National Longitudinal Study of Adolescent to Adult Health. Five-fold cross-validation assessed performance (AUC and E/O ratio). Independent validation on two external datasets. Model provides personalized absolute risk scores for individual patients.

Currently, clinicians have no validated tool to identify which young cannabis users are most likely to develop use disorder. A simple 5-factor model could be integrated into routine clinical screening to enable early, targeted intervention before problems develop.

Moving from population-level risk factors to personalized risk prediction represents a shift toward precision prevention in substance use. A brief personality and behavior assessment could identify the specific young cannabis users who would benefit most from intervention.

Moderate AUC (0.64–0.75) means the model misses some who develop CUD and flags some who don't. Based on an older cohort — cannabis products and patterns have changed. Limited to 5 factors for simplicity, potentially missing important predictors. Requires first cannabis use as entry point.