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

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.

KEYWORDS:

• Data science
• substance use disorder
• alcohol use disorder
• machine learning
• mixed modeling
• biomarker development
• Jupyter notebook

Acknowledgements

We would like to acknowledge the ABQ DrinQ study participants and the ABQ DrinQ study team for collecting the project data and the BioRealm LLC team for computing and software development support.

Disclosure statement

JWB is an owner and employee of BioRealm LLC. BioRealm LLC offers data analytic services.

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website.

Additional information

Funding

This work is funded in part by NIH/NIAAA SBIR Phase I Contract [75N94020C00003]; NIH grants [R01 AA023665, R01 AI121351, P01 CA138338, and U01 CA164973]; NSF grant [OIA-1826715]; and Office of Naval Research Grant [N00014-19-1-2295].