面向可解释疾病诊断的不确定性感知大语言模型
Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis
AI总结：
本文提出ConfiDx，一个通过诊断标准微调开源LLM构建的不确定性感知大语言模型，首次联合解决诊断不确定性识别与解释问题，并在真实数据集上验证了其诊断性能和解释可信度。
摘要：
可解释疾病诊断利用患者信息（如体征和症状）和计算模型生成可能的诊断及推理，具有明确的临床价值。然而，当临床记录包含的证据不足以做出确定诊断时（例如缺乏明确的症状），通常会产生诊断不确定性，从而增加误诊和不良后果的风险。尽管明确识别和解释诊断不确定性对于可信的诊断系统至关重要，但这一方向仍未得到充分探索。为填补这一空白，我们提出了ConfiDx，这是一个不确定性感知的大语言模型（LLM），通过使用诊断标准对开源LLM进行微调而构建。我们对任务进行了形式化定义，并构建了包含丰富标注的数据集，以捕捉不同程度的诊断模糊性。在真实世界数据集上对ConfiDx的评估表明，它在识别诊断不确定性方面表现出色，取得了更优的诊断性能，并为诊断和不确定性生成了可信的解释。据我们所知，这是首个联合处理诊断不确定性识别与解释的研究，显著提升了自动诊断系统的可靠性。
英文摘要：
Explainable disease diagnosis, which leverages patient information (e.g., signs and symptoms) and computational models to generate probable diagnoses and reasonings, offers clear clinical values. However, when clinical notes encompass insufficient evidence for a definite diagnosis, such as the absence of definitive symptoms, diagnostic uncertainty usually arises, increasing the risk of misdiagnosis and adverse outcomes. Although explicitly identifying and explaining diagnostic uncertainties is essential for trustworthy diagnostic systems, it remains under-explored. To fill this gap, we introduce ConfiDx, an uncertainty-aware large language model (LLM) created by fine-tuning open-source LLMs with diagnostic criteria. We formalized the task and assembled richly annotated datasets that capture varying degrees of diagnostic ambiguity. Evaluating ConfiDx on real-world datasets demonstrated that it excelled in identifying diagnostic uncertainties, achieving superior diagnostic performance, and generating trustworthy explanations for diagnoses and uncertainties. To our knowledge, this is the first study to jointly address diagnostic uncertainty recognition and explanation, substantially enhancing the reliability of automatic diagnostic systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
