A Bayesian mixed effects support vector machine for learning and predicting daily substance use disorder patterns. (4th July 2022)

• Record Type: Journal Article
• Authors: Baurley, James W.; Claus, Eric D.; Witkiewitz, Katie; McMahan, Christopher S.
• Is Part Of: American journal of drug and alcohol abuse. Volume 48:Number 4(2022)
• Page Start: 413
• Page End: 421
• Publication Date: 2022-07-04
• Subjects: Data science -- substance use disorder -- alcohol use disorder -- machine learning -- mixed modeling -- biomarker development -- Jupyter notebook; Drug abuse -- Treatment -- Periodicals; Alcoholism -- Treatment -- Periodicals; Substance-abuse -- Treatment -- Periodicals; Alcoholism -- Periodicals; Substance-Related Disorders -- Periodicals; 616.86
• DOI: 10.1080/00952990.2021.2024839
• Languages: English
• ISSNs: 0095-2990

Abstract

ABSTRACT: Background: Substance use disorder (SUD) is a heterogeneous disorder. Adapting machine learning algorithms to allow for the parsing of intrapersonal and interpersonal heterogeneity in meaningful ways may accelerate the discovery and implementation of clinically actionable interventions in SUD research. Objectives: Inspired by a study of heavy drinkers that collected daily drinking and substance use (ABQ DrinQ), we develop tools to estimate subject-specific risk trajectories of heavy drinking; estimate and perform inference on patient characteristics and time-varying covariates; and present results in easy-to-use Jupyter notebooks. Methods: We recast support vector machines (SVMs) into a Bayesian model extended to handle mixed effects. We then apply these methods to ABQ DrinQ to model alcohol use patterns. ABQ DrinQ consists of 190 heavy drinkers (44% female) with 109, 580 daily observations. Results: We identified male gender (point estimate; 95% credible interval: −0.25;-0.29, -0.21), older age (−0.03;-0.03, -0.03), and time varying usage of nicotine (1.68;1.62, 1.73), cannabis (0.05;0.03, 0.07), and other drugs (1.16;1.01, 1.35) as statistically significant factors of heavy drinking behavior. By adopting random effects to capture the subject-specific longitudinal trajectories, the algorithm outperforms traditional SVM (classifies 84% of heavy drinking days correctly versus 73%). Conclusions: We developed a mixed effects variant of SVM and compare it to the traditional formulation, with an eye toward elucidating the importance of incorporating random effects to account for underlying heterogeneity in SUD data. These tools and examples are packaged into a repository for researchers to explore. Understanding patterns and risk of substance use could be used for developing individualized interventions.