Background: Alcohol use disorder (AUD) is a chronic mental disorder that leads to harmful, compulsive drinking patterns that can have serious consequences. Advancements are needed to overcome current barriers in diagnosis and treatment of AUD. Objectives: This comprehensive review analyzes research efforts that apply machine learning (ML) methods for AUD prediction, diagnosis, treatment and health outcomes. Methods: A systematic literature review was conducted. A search performed on 12/02/2020 for published articles indexed in Embase and PubMed Central with AUD and ML-related terms retrieved 1,628 articles. We identified those that used ML-based techniques to diagnose AUD or make predictions concerning AUD or AUD-related outcomes. Studies were excluded if they were animal research, did not diagnose or make predictions for AUD or AUD-related outcomes, were published in a non-English language, only used conventional statistical methods, or were not a research article. Results: After full screening, 70 articles were included in our review. Algorithms developed for AUD predictions utilize a wide variety of different data sources including electronic health records, genetic information, neuroimaging, social media, and psychometric data. Sixty-six of the included studies displayed a high or moderate risk of bias, largely due to a lack of external validation in algorithm development and missing data. Conclusions: There is strong evidence that ML-based methods have the potential for accurate predictions for AUD, due to the ability to model relationships between variables and reveal trends in data. The application of ML may help address current underdiagnosis of AUD and support those in recovery for AUD.

All authors are or have been employees or contractors of Dascena (Houston, Texas, United States).

This study did not receive any public funding.

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