Papers by Jun-En Ding
No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery (2025.findings-emnlp)
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| Challenge: | Deep learning models lacking interpretability and interactivity, authors say . lack of interactive mechanisms prevents clinicians from incorporating their own knowledge into decision-making process. |
| Approach: | a new deep learning model is proposed to improve interpretability and interactivity . authors propose a knowledge-enhanced agent-driven causal discovery framework . |
| Outcome: | a new model improves interpretability and interactivity on EHR data . the proposed model improve interpretability through explicit reasoning and causal analysis . |
MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation (2025.acl-industry)
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Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang, Zitao Shuai, Thao Nguyen Minh Phan, Jun-En Ding, Chun-Chieh Liao, Pengfei Hu, Xiaoxue Han, Chih-Ho Hsu, Dongsheng Luo, Wen-Chih Peng, Feng Liu, Fang-Ming Hung, Chenwei Wu
| Challenge: | Existing systems focus primarily on assessment rather than treatment planning. |
| Approach: | They propose a framework that structures LLM reasoning to align with real-life workflows. |
| Outcome: | The proposed framework outperforms baseline approaches in assessment accuracy and treatment plan quality. |