EviGen: Predictive Evidence Scaffolding for Verifiable Clinical Rationale Generation

2026-09-16

AI Summary

为解决临床记录审查难题，EviGen通过三层框架，包括证据检索、生成和验证，来生成可验证的临床推理，提高预测性能和推理准确性。

Abstract

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 prediction attribution scores. The second layer is an LLM generator that consumes this ranked evidence as a scaffold to produce a clinical rationale grounded in the retrieved spans. The third layer is a process-supervised verifier that checks the generated rationale at the reasoning-step level, flagging unreliable claims. Across three medical prediction datasets, EviGen improves prediction performance and rationale faithfulness over full-context LLM and RAG baselines, and is preferred by clinical reviewers in a usability evaluation.

Problem

• Longitudinal EHRs
• Clinical Rationale
• Predictive Evidence
• LLM-based Processing
• Rationale Generation

Innovation

• EviGen
• Predictive Evidence Scaffolding
• Clinical Rationale Generation
• Patient-Conditioned Retriever
• Process-Supervised Verifier