从临床文件中使用LLM实现专家级临床感染检测:在复杂的肝硬化患者病例中进行验证
medRxiv : the preprint server for health sciences
|February 6, 2026
概括
一个新的大型语言模型 (LLM) 准确地识别了肝硬化患者的感染,优于传统方法. 这种人工智能工具增强了感染监测和临床研究,以获得更好的患者结果.
科学领域:
- 肝病学和传染病的研究
- 人工智能在医学中的应用
- 临床信息学 临床信息学
背景情况:
- 系统性感染是肝硬化患者死亡的主要原因.
- 目前的感染识别方法 (ICD-10,手册审查) 是不可靠和低效的.
- 准确的感染检测对于患者护理和肝硬化预测建模至关重要.
研究的目的:
- 开发和验证基于大型语言模型 (LLM) 的方法,用于自动化感染分类和亚型化.
- 为了提高在急诊室 (ED) 肝硬化患者中识别感染的准确性和效率.
- 克服现有的感染监测方法在这个人群中的局限性.
主要方法:
- 开发了INFEHR (使用自由文本EHR分析进行感染识别和亚型化),使用Claude 3.5 Sonnet.net开发了一个LLM管道.
- 分析了来自1000名肝硬化患者ED遭遇的72小时临床记录.
- 与医生判断的数据,ICD-10代码和CDC败血症标准相比较INFEHR的表现.
主要成果:
- INFEHR实现了94.7%的整体准确性,99.5%的灵敏度和92.8%的感染检测PPV.
- 在所有指标上,LLM显著超过ICD-10分类 (p < 0.0001).
- 在病原体类型和感染部位分类方面,INFEHR表现出高准确性,快速处理笔记.
结论:
- INFEHR提供了一种可扩展,可复制和准确的方法,用于肝硬化中的感染表型.
- 该LLM克服了传统编码和手动审查的局限性,用于高通量监控.
- INFEHR为研究和潜在的实时临床决策支持提供了改进的队列构建.
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