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Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
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Craving for a Robust Methodology: A Systematic Review of Machine Learning Algorithms on Substance-Use Disorders

Bernardo Paim de Mattos1,2, Christian Mattjie2, Rafaela Ravazio2

  • 1Developmental Cognitive Neuroscience Lab, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Rio Grande do Sul Brazil.

International Journal of Mental Health and Addiction
|May 7, 2026
PubMed
Summary

Machine learning (ML) shows promise for predicting substance use disorder (SUD) treatment outcomes, improving clinical decisions. However, research needs standardized methods and better validation for reliable application in SUD care.

Keywords:
Machine learningPrediction modelsSubstance-related disordersSystematic reviewTreatment outcome

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Area of Science:

  • Psychiatry and Mental Health
  • Computational Medicine
  • Health Informatics

Background:

  • Substance use disorders (SUDs) present complex challenges in treatment due to chronicity and variable responses.
  • Effective prediction of treatment outcomes is crucial for personalized SUD care.
  • Machine learning (ML) offers potential tools for analyzing and predicting SUD treatment trajectories.

Purpose of the Study:

  • To systematically review the application of ML algorithms in predicting and analyzing treatment outcomes for SUDs.
  • To identify the strengths and limitations of current ML approaches in SUD treatment research.
  • To provide recommendations for future research and clinical implementation.

Main Methods:

  • Systematic literature search conducted across PubMed, Embase, Scopus, and Web of Science.
  • Inclusion criteria applied to 362 initial articles, resulting in 28 reviewed studies.
  • Methodological quality and bias assessed using MI-CLAIM and CHARMS instruments.

Main Results:

  • ML models demonstrate significant potential in enhancing predictive accuracy for SUD treatment adherence, relapse, and severity.
  • Reviewed studies primarily focused on opioid, cocaine, and alcohol use disorders.
  • Identified critical gaps in methodological consistency, transparency, and external validation across studies.

Conclusions:

  • ML holds considerable promise for improving clinical decision-making in SUD treatment.
  • Standardized protocols and best practices are essential for the consistent and reliable application of ML in SUD research.
  • Future research should prioritize methodological rigor and external validation to translate ML findings into clinical practice.