多证据临床推理与紧急选的检索增强生成:回顾性评估研究研究
Hang Sheung Wong1, Tsz Kwan Wong2
1Department of Accident & Emergency, Princess Margaret Hospital, Hong Kong (SAR), China (Hong Kong).
JMIR medical informatics
|January 26, 2026
概括
一个新的双源检索增强生成 (RAG) 系统提高了紧急分拣的准确性,超过了基线大语言模型 (LLM). 这个系统,MECR-RAG,减少了过量选和更好地识别高风险患者,与专家的共识保持一致.
科学领域:
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
- 紧急医疗 紧急医疗
背景情况:
- 紧急分类准确性至关重要,但临床医生的经验,认知负载和病例复杂性提出了挑战.
- 错误的分离可以导致高风险患者的护理延迟,并增加急诊室拥挤.
- 大型语言模型 (LLM) 提供了对分类决策支持的潜力,但在可靠性和幻觉方面面临挑战.
研究的目的:
- 评估一个双源检索增强生成 (RAG) 系统,整合指导方针和病例证据,用于紧急分类.
- 将RAG系统的性能与基线LLM进行比较.
- 评估RAG系统紧急任务与专家共识和临床严重性的协调程度.
主要方法:
- 开发一个双源RAG系统 (MECR-RAG) 使用香港事故和紧急选指南和3000个选案例.
- 对MECR-RAG的回顾性评估和对236次分拣遭遇的即时的LLM.
- 系统预测与专家共识标签 (高级分拣护士) 的比较,使用二次加权卡帕 (QWK) 和准确度.
主要成果:
- 与基线LLM (QWK 0.801,准确率为0.542) 相比,MECR-RAG实现了更高的性能 (QWK 0.902,准确率为0.802).
- MECR-RAG显著减少了过量选 (12.7%与28.8%相比),同时保持了较低的不足选 (1.3%与1.7%相比).
- 与初始护士分拣相比,该系统在检测高风险患者方面表现出更好的灵敏度.
结论:
- 双源RAG系统 (MECR-RAG) 在紧急分拣方面达成了与专家相似的协议.
- 与基线LLM相比,MECR-RAG减少了过度选和改善了紧急分配对齐.
- 建议进行进一步的前性研究,以确认对患者流动和结果的影响.
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