EviGen improves clinical outcome predictions with verifiable evidence
EviGen: Predictive Evidence Scaffolding for Verifiable Clinical Rationale Generation
Computation and Language
Summary
Long-term patient records hold lots of valuable clues for predicting medical outcomes, but reading all this information is too time-consuming for doctors. The authors built EviGen to automatically find the most predictive pieces of evidence from patient histories and then generate clear explanations for clinical predictions. This system also double-checks its own reasoning to avoid mistakes. When tested, EviGen gave better predictions and explanations than other AI methods and was preferred by medical reviewers.
What this means in practice
- •For hospital data teams: Integrate EviGen to improve accuracy and trustworthiness of clinical outcome predictions by providing transparent evidence-based rationales.
- •For healthcare ai engineers: Develop clinical decision support tools that generate verifiable patient-specific explanations from complex medical records.
Authors
Fengnan Li, Heman Burre, Liwen Sun, Roshni Varma, Matthew M. Engelhard
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.