Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity

BACKGROUND: This longitudinal study explored the utility of machine learning (ML) methodology in predicting the trajectory of severity of substance use from childhood to thirty years of age using a set of psychological and health characteristics. DESIGN: Boys (N=494) and girls (N=206) were recruited using a high-risk paradigm at 10–12 years of age and followed up at 12–14, 16, 19, 22, 25 and 30 years of age. MEASUREMENTS: At each visit, the subjects were administered a comprehensive battery to measure psychological makeup, health status, substance use and psychiatric disorder, and their overall harmfulness of substance consumption was quantified according to the multidimensional criteria (physical, dependence, and social) developed by Nutt et al. (2007). Next, high- and low- substance use severity trajectories were derived differentially associated with probability of segueing to substance use disorder (SUD). ML methodology was employed to predict trajectory membership. FINDINGS: The high-severity trajectory group had a higher probability of leading to SUD than the low-severity trajectory (89.0% vs 32.4%; odds ratio=16.88, p