{"ID":2878824,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17207","arxiv_id":"2508.17207","title":"Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)","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.","short_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 as...","url_abs":"https://arxiv.org/abs/2508.17207","url_pdf":"https://arxiv.org/pdf/2508.17207v1","authors":"[\"Xinyu Qin\",\"Mark H. Chignell\",\"Alexandria Greifenberger\",\"Sachinthya Lokuge\",\"Elssa Toumeh\",\"Tia Sternat\",\"Martin Katzman\",\"Lu Wang\"]","published":"2025-08-24T04:14:48Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}