EviGen: Predictive Evidence Scaffolding for Verifiable Clinical Rationale Generation
Longitudinal electronic health records (EHRs) capture years of patient history across notes, codes, labs, and procedures, and contain evidence needed to reason about likely clinical outcomes. However, comprehensive clinician review of these records is impractical, and LLM-based processing is costly and often unreliable, missing some relevant observations while hallucinating others. We therefore propose EviGen, a three-layer framework for verifiable clinical rationale generation that addresses these challenges. The first layer is a patient-conditioned retriever that uses learnable queries to find evidence predictive of, not just textually relevant to, a clinical outcome and ranks it by pre...
| Authors | Fengnan Li, Heman Burre, Liwen Sun et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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