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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.17207 |
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| _version_ | 1866908500129480704 |
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| author | Qin, Xinyu Chignell, Mark H. Greifenberger, Alexandria Lokuge, Sachinthya Toumeh, Elssa Sternat, Tia Katzman, Martin Wang, Lu |
| author_facet | Qin, Xinyu Chignell, Mark H. Greifenberger, Alexandria Lokuge, Sachinthya Toumeh, Elssa Sternat, Tia Katzman, Martin Wang, Lu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17207 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population) Qin, Xinyu Chignell, Mark H. Greifenberger, Alexandria Lokuge, Sachinthya Toumeh, Elssa Sternat, Tia Katzman, Martin Wang, Lu Artificial Intelligence 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. |
| title | Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population) |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.17207 |