用大模型自动选临床决策规则，帮医生快速做对诊断。
CDR-Agent: Intelligent Selection and Execution of Clinical Decision Rules Using Large Language Model Agents
- 大模型分析病历文本，自动匹配最合适的临床决策规则。
- 在合成数据上比纯大模型提升56.3%准确率，真实数据也显著更优。
- 适合急诊、需要快速精准决策的临床场景，减少误诊漏诊。
临床决策复杂且节奏快，尤其在急诊科（ED）中需迅速做出高风险判断。临床决策规则（CDRs）是基于证据的标准化工具，通过整合体征、症状和临床变量形成决策路径，提升诊断一致性与准确性。但其应用常受限于医生认知负荷，难以快速回忆与使用。我们提出 CDR-Agent，一个基于大语言模型（LLM）的系统，可自主从非结构化病历中识别并执行最恰当的 CDR。为验证该系统，我们构建了两个新数据集：合成数据集与 CDR-Bench。实验表明，相较于独立使用的 LLM 基线，CDR-Agent 在合成数据上实现 56.3% 的准确率提升，在 CDR-Bench 上提升 8.7%。此外，系统显著降低计算开销。结果还显示，它能高效选择相关规则，并在影像决策中保持谨慎，减少不必要的检查，同时有效识别多数阳性病例，优于传统提示工程方法。代码已开源。
原文摘要 · Abstract (English)
Clinical decision-making is inherently complex and fast-paced, particularly in emergency departments (EDs) where critical, rapid and high-stakes decisions are made. Clinical Decision Rules (CDRs) are standardized evidence-based tools that combine signs, symptoms, and clinical variables into decision trees to make consistent and accurate diagnoses. CDR usage is often hindered by the clinician's cognitive load, limiting their ability to quickly recall and apply the appropriate rules. We introduce CDR-Agent, a novel LLM-based system designed to enhance ED decision-making by autonomously identifying and applying the most appropriate CDRs based on unstructured clinical notes. To validate CDR-Agent, we curated two novel ED datasets: synthetic and CDR-Bench, although CDR-Agent is applicable to non ED clinics. CDR-Agent achieves a 56.3\% (synthetic) and 8.7\% (CDR-Bench) accuracy gain relative to the standalone LLM baseline in CDR selection. Moreover, CDR-Agent significantly reduces computational overhead. Using these datasets, we demonstrated that CDR-Agent not only selects relevant CDRs efficiently, but makes cautious yet effective imaging decisions by minimizing unnecessary interventions while successfully identifying most positively diagnosed cases, outperforming traditional LLM prompting approaches. Code for our work can be found at: https://github.com/zhenxianglance/medagent-cdr-agent
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