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March 6, 2026BMC Medical Informatics and Decision MakingOpen Access

Explainable counterfactual reasoning in depression medication selection at multi-levels (personalized and population)

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Authors

XQXinyu QinMCMark ChignellAGAlexandria Greifenberger

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Overview

Demonstrates predictive modeling for medication selection in depression, indicating significant implications for treatment personalization.

Key Points

  • This research aims to explore how symptom variations in Major Depressive Disorder influence medication selection through predictive modeling.
  • Analyzed associations between MDD symptoms and RCT arm assignments for SSRIs and SNRIs.
  • Applied explainable counterfactual reasoning to assess symptom changes on model predictions.
  • Utilized 17 classifiers with CatBoost achieving the best performance metrics.
  • Assessed local and global feature importance of symptoms in medication selection.
  • Achieved typical test metrics between 0.74 and 0.78, with a best ROC-AUC of 0.7640.
  • Identified specific MDD symptoms used by the model to distinguish between SSRI and SNRI assignments.
  • Highlighted the need for prospective validation in real-world settings.

Cite This Study

Qin et al. (2026) studied this question.

synapsesocial.com/papers/69aa70e7531e4c4a9ff5b1f5https://doi.org/10.1186/s12911-026-03403-6
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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  3. 3Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study2024 · 15 citations
  4. 4Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets2024 · 13 citations
  5. 5Systematic Review and Meta-Analysis of Explainable Machine Learning Models for Clinical Depression Detection2025