Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review

Clinical Snapshot

Key Findings

• P Value: Not applicable for systematic review

• Effect Size: AUC range 0.59-0.95, classification accuracy 62%-95.4%

• Primary Outcome: ML model performance for comparative antidepressant selection

• Nnt Or Sensitivity: Only 1 of 19 studies implemented unified framework for patient-level treatment ranking

• Confidence Interval: Not reported as meta-analysis not conducted

Clinical Application

Currently limited - technology remains in early development stage with significant barriers to clinical implementation Relevant to Australian practice given similar MDD prevalence and PBS antidepressant options, but requires local validation studies and integration with existing clinical decision support systems Adults with MDD requiring antidepressant selection, though generalizability uncertain due to limited external validation

Abstract

BACKGROUND: Major depressive disorder (MDD) affects approximately 1 in 6 adults during their lifetime, yet antidepressant selection relies predominantly on trial-and-error, with response rates of only 42% to 53%. While machine learning (ML) models have shown promise in predicting treatment outcomes, most focus on single treatments rather than comparative selection across therapeutic alternatives, limiting their clinical utility for the medication choice decisions that clinicians face in practice. OBJECTIVE: This systematic review evaluates ML approaches that examine 2 or more pharmacological interventions for predicting treatment outcomes in MDD, with a focus on their capacity to facilitate comparative treatment selection between medications or medication classes for individual patients. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched PubMed, Scopus, and Web of Science for studies published from 2015 to 2025. We included studies involving adults with MDD that used ML models to predict treatment outcomes across 2 or more pharmacological treatments and reported medication-specific prediction outcomes. Risk of bias was assessed using PROBAST-AI (Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence). We conducted a narrative synthesis organized by modeling strategies, data integration approaches, validation methodologies, and performance patterns. RESULTS: From 5370 initial records, 19 studies met the inclusion criteria, with dataset sample sizes ranging from 49 to 77,226 participants. Studies employed 3 distinct modeling strategies: drug-specific supervised models trained independently for each medication, subtype- or trajectory-based approaches using clustering methods to identify differential response patterns, and a unified differential prediction framework generating calibrated cross-treatment predictions. Performance varied substantially, with area under the curve values ranging from 0.59 to 0.95 and classification accuracies between 62% and 95.4%, though high performance was concentrated in studies with small samples, high-dimensional neurobiological features, and internal-only validation. Only 7 studies conducted external validation, which generally yielded more conservative performance estimates. Feature informativeness was more consistently associated with performance variation than algorithm complexity. Most studies did not formally distinguish between prognostic features predicting general outcomes and predictive features identifying differential medication responses, and none applied formal explainability techniques. CONCLUSIONS: ML for comparative antidepressant selection remains in an early stage of development. Only 1 study implemented a unified framework directly supporting patient-level treatment ranking. Key barriers to clinical translation include insufficient distinction between prognostic and predictive markers, limited cross-trial validation, near-absent calibration reporting, and absent explainability. Future research should prioritize unified comparative frameworks with calibrated predictions, rigorous external validation on diverse cohorts, explicit modeling of heterogeneous treatment effects, and integration of explainability into model development.

References

1. 1.He, F., Huang, S., Wang, R., Chang, A., Phillips, J. L., & Sun, C. (2026). Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review. JMIR Mental Health. https://doi.org/10.2196/89352