使用大型语言模型和基于知识图的检索增强生成来检测患者门户消息中的紧急情况
Siru Liu1,2, Aileen P Wright1,3, Allison B McCoy1
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37212, United States.
概括
与知识图集集成的大型语言模型 (LLM) 可以有效地分类紧急患者信息. 这种方法可以通过识别紧急病例来提高患者的安全性,以便及时获得医疗护理.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
- 临床决策支持系统 临床决策支持系统
背景情况:
- 通过门户网站发送的患者信息往往需要紧急关注,如果不及时识别,可能会构成患者安全风险.
- 传统的对患者信息进行分类的方法可能耗时,可能会延迟关键护理.
- 大型语言模型 (LLM) 提供了自动化消息分析的潜力,但需要特定领域的知识才能准确.
研究的目的:
- 开发和评估一种使用LLM和知识图表进行紧急患者信息分类的方法.
- 通过在紧急情况下及时通知,提高患者安全.
- 当门户消息显示紧急情况时,引导患者寻求立即的医疗帮助.
主要方法:
- 开发了四种模型:Prompt-Only LLM,Naïve Retrieval Augmented Generation (RAG),RAG来自知识图与本地搜索,RAG来自知识图与全球搜索.
- 使用了来自范德比尔特大学医学中心的1020个患者信息的数据集.
- 在RAG模型的基础上,使用了一个来自225个协议的护士分拣手册的知识库.
主要成果:
- 来自知识图模型的RAG与全球搜索展示了卓越的性能.
- 该模型实现了0.99的精度,0.98的灵敏度和0.99.9的特异性.
- 与没有RAG和原始RAG的LLM相比,观察到对紧急消息进行分类的显著改进.
结论:
- 增加特定领域知识的LLM,特别是通过结构化知识图表,提高了患者信息分类的性能.
- 通过将LLM与护士分类知识图相结合,可以有效地识别紧急病人的通信.
- 未来的工作应该包括扩展知识图和评估系统对患者结果的实际影响.
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