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Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review
Fiona He1, Steven Huang2,3, Richard Wang4
1Digital Transformation and Innovation, Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada.
JMIR Mental Health
|May 13, 2026
Summary
Machine learning (ML) shows promise for selecting antidepressants for major depressive disorder (MDD), but comparative prediction models are still developing. Future research needs unified frameworks, external validation, and explainability for clinical use.
Area of Science:
- * Computational psychiatry
- * Clinical informatics
- * Pharmacogenomics
Background:
- * Major depressive disorder (MDD) affects a significant portion of the adult population, yet treatment selection remains largely empirical.
- * Current antidepressant selection methods have limited efficacy, with response rates between 42% and 53%.
- * Existing machine learning (ML) models often predict outcomes for single treatments, not facilitating comparative selection.
Conclusions:
- * ML for comparative antidepressant selection is nascent, with few studies directly supporting patient-level treatment ranking.
- * Clinical translation is hindered by a lack of distinction between prognostic and predictive markers, limited external validation, and absent explainability.
- * Future research should focus on unified comparative frameworks, rigorous external validation, and integrating explainability for improved clinical utility.
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