标题： 通过置信度估计的电子健康记录可信代理 显示英文标题

标题： Trustworthy Agents for Electronic Health Records through Confidence Estimation

摘要： 大型语言模型（LLMs）在从电子健康记录（EHR）中提取信息和支持临床决策方面显示出前景。 然而，在临床环境中部署面临由于幻觉风险带来的挑战。 我们提出了Hallucination Controlled Accuracy at k%（HCAcc@k%），一种新型指标，用于量化不同置信度阈值下的准确性-可靠性权衡。 我们引入了TrustEHRAgent，一个具有置信度意识的代理，结合逐步置信度估计来进行临床问题回答。 在MIMIC-III和eICU数据集上的实验表明，TrustEHRAgent在严格的可靠性约束下优于基线方法，在HCAcc@70%时分别实现了44.23%p和25.34%p的提升，而基线方法在这些阈值上失败。 这些结果突显了传统准确性指标在评估医疗AI代理方面的局限性。 我们的工作有助于开发可信赖的临床代理，能够在置信度低时提供准确信息或透明地表达不确定性。 显示英文摘要

摘要： Large language models (LLMs) show promise for extracting information from Electronic Health Records (EHR) and supporting clinical decisions. However, deployment in clinical settings faces challenges due to hallucination risks. We propose Hallucination Controlled Accuracy at k% (HCAcc@k%), a novel metric quantifying the accuracy-reliability trade-off at varying confidence thresholds. We introduce TrustEHRAgent, a confidence-aware agent incorporating stepwise confidence estimation for clinical question answering. Experiments on MIMIC-III and eICU datasets show TrustEHRAgent outperforms baselines under strict reliability constraints, achieving improvements of 44.23%p and 25.34%p at HCAcc@70% while baseline methods fail at these thresholds. These results highlight limitations of traditional accuracy metrics in evaluating healthcare AI agents. Our work contributes to developing trustworthy clinical agents that deliver accurate information or transparently express uncertainty when confidence is low.