Absolute Risk Prediction for Cannabis Use Disorder in Adolescence and Early Adulthood Using Bayesian Machine Learning

TL;DR

This study introduces a Bayesian machine learning model that predicts the absolute risk of developing cannabis use disorder in adolescents and young adults, aiding targeted interventions based on personalized risk factors.

Contribution

The paper presents a novel Bayesian risk prediction model for CUD that incorporates five key risk factors and demonstrates good predictive performance across multiple datasets.

Findings

Model achieved AUC up to 0.75 in validation datasets.

Five risk factors identified: sex, delinquency, personality traits.

Model shows good calibration and discrimination.

Abstract

Introduction: Substance use disorders (SUDs) have emerged as a pressing public health concern in the United States, with adolescent substance use often leading to SUDs in adulthood. Effective strategies are needed to stem this progression. To help fulfill this need, we developed a novel absolute risk prediction model for cannabis use disorder (CUD) for adolescents or young adults who use cannabis. Methods: We trained a Bayesian machine learning model that provides a personalized CUD absolute risk for adolescents or young adults who use cannabis with data from the National Longitudinal Study of Adolescent to Adult Health. Model performance was 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). Independent validation of the final model was conducted using two 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 values for the training dataset and two validation datasets were 0.68, 0.64, and 0.75, respectively, and E/O values were 0.95, 0.98, and 1, respectively. This indicates good discrimination and calibration performance of the model. Discussion and Conclusion: The proposed model can aid clinicians in assessing the risk of developing CUD among adolescents and young adults who use cannabis, enabling clinically appropriate interventions.