用反事实推理分析抑郁症状如何影响抗抑郁药选择
Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)
- 通过反事实解释分析症状变化对用药决策的影响
- 随机森林模型准确率等指标达0.85左右，表现最佳
- 帮助医生理解AI决策依据，适合临床AI系统开发者
背景：本研究探讨了重度抑郁障碍（MDD）症状变化（以汉密尔顿抑郁量表，HAM-D量化）如何因果影响选择舍曲林类药物（SSRIs）或文拉法辛类药物（SNRIs）。方法：采用可解释的反事实推理与反事实解释（CFs），评估特定症状变化对药物选择的影响。结果：在17个二分类器中，随机森林模型表现最优，准确率、F1值、精确率、召回率及ROC-AUC均接近0.85。基于样本的反事实解释揭示了个体症状在用药决策中的局部与全局重要性。结论：反事实推理阐明了哪些MDD症状最显著驱动SSRI与SNRI的选择，提升了基于AI的临床决策支持系统的可解释性。未来工作应在更多样化的队列中验证这些发现，并优化算法以支持临床部署。
原文摘要 · Abstract (English)
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.
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