Applications of machine learning in addiction studies: A systematic review

• Published in: Psychiatry research Vol. 275; pp. 53 - 60
• Main Authors: Mak, Kwok Kei, Lee, Kounseok, Park, Cheolyong
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.05.2019
• Subjects: Addiction; Algorithms; Behavior, Addictive - psychology; Behavior, Addictive - therapy; Cross-Over Studies; Cross-Sectional Studies; Gambling - psychology; Gambling - therapy; Humans; Machine learning; Machine Learning - trends; Reinforcement learning; Smoking - psychology; Smoking - therapy; Substance-Related Disorders - psychology; Substance-Related Disorders - therapy; Supervised learning; Unsupervised learning; Addiction; Supervised learning; Unsupervised learning; Machine learning; Reinforcement learning
• ISSN: 0165-1781; 1872-7123
• DOI: 10.1016/j.psychres.2019.03.001

•Seventeen studies with substance and non-substance uses as the addiction outcomes were included in the final review.•Relatively more studies employed supervised learning than unsupervised learning or reinforcement learning in data analyses.•Supervised ensemble methods or multiple algorithm comparisons were used to enhance the accuracy of predictions of addictive behaviors.•Machine learning could potentially promote precision psychiatry. This study aims to provide a systematic review of the applications of machine learning methods in addiction research. In this study, multiple searches on MEDLINE, Embase and the Cochrane Database of Systematic Reviews were performed. 23 full-text articles were assessed and 17 articles met the inclusion criteria for the final review. The selected studies covered mainly substance addiction (N = 14, 82.4%), including smoking (N = 4), alcohol drinking (N = 3), as well as uses of cocaine (N = 4), opioids (N = 1), and multiple substances (N = 2). Other studies were non-substance addiction (N = 3, 17.6%), including gambling (N = 2) and internet gaming (N = 1). There were eight cross-sectional, seven cohort, one non-randomized controlled, and one crossover trial studies. Majority of the studies employed supervised learning (N = 13), and others employed unsupervised learning (N = 2) and reinforcement learning (N = 2). Among the supervised learning studies, five studies used ensemble learning methods or multiple algorithm comparisons, six used regression, and two used classification. The two included reinforcement learning studies used the direct methods. These results suggest that machine learning methods, particularly supervised learning are increasingly used in addiction psychiatry for informing medical decisions.