A Bayesian mixed effects support vector machine for learning and predicting daily substance use disorder patterns

• Published in: The American journal of drug and alcohol abuse Vol. 48; no. 4; pp. 413 - 421
• Main Authors: Baurley, James W., Claus, Eric D., Witkiewitz, Katie, McMahan, Christopher S.
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 04.07.2022; Taylor & Francis Ltd
• Subjects: Alcohol use; alcohol use disorder; Algorithms; Bayes Theorem; Bayesian analysis; biomarker development; Data science; Drinking behavior; Drug use; Female; Humans; Intervention; Jupyter notebook; machine learning; Machinery; Male; Marijuana; mixed modeling; Nicotine; Parsing; Random effects; Substance abuse; Substance use disorder; Substance-Related Disorders - epidemiology; Support Vector Machine; Support vector machines; Jupyter notebook; biomarker development; Data science; alcohol use disorder; substance use disorder; mixed modeling; machine learning
• ISSN: 0095-2990; 1097-9891; 1097-9891
• DOI: 10.1080/00952990.2021.2024839

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.