Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)

TL;DR

This paper uses explainable counterfactual reasoning to identify key depressive symptoms influencing antidepressant choice, improving interpretability of AI decision support in depression treatment.

Contribution

It introduces a novel application of counterfactual explanations to understand symptom-driven medication decisions in depression.

Findings

Random Forest classifier achieved high accuracy (~0.85).

Counterfactuals reveal symptom importance in medication choice.

Method enhances interpretability of AI in clinical settings.

Abstract

Background: This study investigates how variations in Major Depressive Disorder (MDD) symptoms, quantified by the Hamilton Rating Scale for Depression (HAM-D), causally influence the prescription of SSRIs versus SNRIs. Methods: We applied explainable counterfactual reasoning with counterfactual explanations (CFs) to assess the impact of specific symptom changes on antidepressant choice. Results: Among 17 binary classifiers, Random Forest achieved highest performance (accuracy, F1, precision, recall, ROC-AUC near 0.85). Sample-based CFs revealed both local and global feature importance of individual symptoms in medication selection. Conclusions: Counterfactual reasoning elucidates which MDD symptoms most strongly drive SSRI versus SNRI selection, enhancing interpretability of AI-based clinical decision support systems. Future work should validate these findings on more diverse cohorts and refine algorithms for clinical deployment.