Absolute Risk Prediction for Cannabis Use Disorder Using Bayesian Machine Learning

• URL: http://arxiv.org/abs/2501.09156v1
• Date: Wed, 15 Jan 2025 21:17:02 GMT
• Title: Absolute Risk Prediction for Cannabis Use Disorder Using Bayesian Machine Learning
• Authors: Tingfang Wang, Joseph M. Boden, Swati Biswas, Pankaj K. Choudhary,
• Abstract summary: The proposed model is the first absolute risk prediction model for an SUD. It can aid clinicians in identifying adolescent/youth substance users at a high risk of developing CUD in future.
• Score: 0.0
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• Abstract: Introduction: Substance use disorders (SUDs) have emerged as a pressing public health crisis in the United States, with adolescent substance use often leading to SUDs in adulthood. Effective strategies are needed to prevent this progression. To help in filling this need, we develop a novel and the first-ever absolute risk prediction model for cannabis use disorder (CUD) for adolescent or young adult cannabis users. Methods: We train a Bayesian machine learning model that provides a personalized CUD absolute risk for adolescent or young adult cannabis users using data from the National Longitudinal Study of Adolescent to Adult Health. Model performance is assessed using 5-fold cross-validation (CV) with area under the curve (AUC) and ratio of the expected to observed number of cases (E/O). External validation of the final model is conducted using two independent datasets. Results: The proposed model has five risk factors: biological sex, delinquency, and scores on personality traits of conscientiousness, neuroticism, and openness. For predicting CUD risk within five years of first cannabis use, AUC and E/O, computed via 5-fold CV, were 0.68 and 0.95, respectively. For the same type of prediction in external validation, AUC values were 0.64 and 0.75, with E/O values of 0.98 and 1, indicating good discrimination and calibration performances of the model. Discussion and Conclusion: The proposed model is the first absolute risk prediction model for an SUD. It can aid clinicians in identifying adolescent/youth substance users at a high risk of developing CUD in future for clinically appropriate interventions.

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