Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)
This provides interpretable AI support for clinical depression medication decisions, though it's incremental as it applies existing counterfactual methods to a specific medical domain.
This study investigated how specific depression symptom changes influence antidepressant selection (SSRIs vs. SNRIs) using explainable counterfactual reasoning, finding that Random Forest classifiers achieved near 0.85 performance metrics and revealing which symptoms most strongly drive medication choices.
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.