Craving for a Robust Methodology: A Systematic Review of Machine Learning Algorithms on Substance-Use Disorders...

Substance use disorders (SUDs) pose significant mental health challenges due to their chronic nature, health implications, impact on quality of life, and variability of treatment response. This systematic review critically examines the application of machine learning (ML) algorithms in predicting and analyzing treatment outcomes in SUDs. Conducting a thorough search across PubMed, Embase, Scopus, and Web of Science, we identified 28 studies that met our inclusion criteria from an initial pool of 362 articles. The MI-CLAIM and CHARMS instruments were utilized for methodological quality and bias assessment. Reviewed studies encompass an array of SUDs, mainly opioids, cocaine, and alcohol use, predicting outcomes such as treatment adherence, relapse, and severity assessment. Our analysis reveals a significant potential of ML models in enhancing predictive accuracy and clinical decision-making in SUD treatment. However, we also identify critical gaps in methodological consistency, transparency, and external validation among the studies reviewed. Our review underscores the necessity for standardized protocols and best practices in applying ML within SUD while providing recommendations and guidelines for future research.

The online version contains supplementary material available at 10.1007/s11469-024-01403-z.