ULoR: Uncertainty-Aware Leave-One-Out Refinement Framework for Diagnosis Prediction

Recent advances in clinical prediction leverage large language models (LLMs) to extract semantic information from Electronic Health Records (EHRs). However, LLMs could produce biased or hallucinated responses camouflaged by their fluency and realistic appearance, which is unacceptable in diagnosis prediction. Uncertainty estimation (UE) has emerged as an effective approach to address this challenge by quantifying hallucination levels and prediction confidence in LLM outputs. Yet, directly determining diagnosis predictions based on UE remains insufficient, as diagnoses with high uncertainty may still correspond to correct outcomes. To this end, we propose ULoR, an uncertainty-aware leave-one-out refinement framework for reliable diagnosis prediction. Specifically, we first compute the UE scores by integrating statistical information from multiple samples of the model's diagnosis ranking distributions and leverage these scores for initial predictions. Then, guided by the leave-one-out strategy, we construct multiple-choice tasks for high-uncertainty diagnoses using external syndrome knowledge and fine-tune the refinement component to resolve them, thereby confirming or replacing uncertain predictions. Extensive experiments on two real-world EHR datasets demonstrate that ULoR consistently outperforms state-of-the-art baselines, showcasing its practical utility in real-world clinical settings.