整合大型语言模型与人类专业知识,用于在电子健康记录中检测疾病
Jie Pan1, Seungwon Lee2, Cheligeer Cheligeer3
1Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Libin Cardiovascular Institute, University of Calgary, Calgary, AB, Canada.
Computers in biology and medicine
|April 8, 2025
概括
本研究引入了一种高效的大型语言模型 (LLM) 管道,用于从电子健康记录 (EHR) 中识别多种疾病,改进了疾病监测的传统方法.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床数据分析 临床数据分析
背景情况:
- 电子健康记录 (EHR) 为疾病监测和医疗保健评估提供了有价值的数据.
- 用于条件识别的EHR数据的手动标签是劳动密集型和耗时的.
- 现有的基于行政数据的方法在获取详细的临床信息方面存在局限性.
研究的目的:
- 利用大型语言模型 (LLM) 开发一种高效的策略,从EHR临床笔记中识别多种疾病.
- 评估基于LLM的管道用于检测急性心肌梗塞 (AMI),糖尿病和高血压的性能.
- 将LLM方法与临床医生验证的诊断和国际疾病分类 (ICD) 代码进行比较.
主要方法:
- 2015年的心脏注册表队列与加拿大阿尔伯塔省的EHR系统联系在一起.
- 开发了一个生成的LLM管道来分析EHR笔记,使用基于诊断,治疗和临床指南的提示.
- 该管道用于检测AMI,糖尿病和高血压,性能与参考标准相比.
主要成果:
- 该LLM管道证明了疾病检测的不同性能指标:AMI (88%的灵敏度,63%的特异性),糖尿病 (91%的灵敏度,86%的特异性) 和高血压 (94%的灵敏度,32%的特异性).
- 与ICD代码相比,基于LLM的方法在所有评估条件中显示出更好的灵敏度和负预测值.
- 在LLM检测的病例和参考标准之间观察到一致的月度趋势模式.
结论:
- 基于LLM的管道提供了一种高效且相当准确的方法,用于从EHR中检测多种条件.
- 这种方法整合了人类专家的知识,而不需要手工策划的标签,简化了EHR分析.
- 开发的方法有可能使用电子健康记录实现全面的实时疾病监测.
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