从电子健康记录中自动化失眠表型化:利用大型语言模型来解码临床叙述
medRxiv : the preprint server for health sciences
|June 12, 2025
概括
本研究引入了一种使用大型语言模型 (LLM) 识别失眠的自动化方法,用于从临床笔记中识别失眠. 这种方法改善了在电子健康记录 (EHR) 中对失眠的记录,以便进行更好的研究.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 临床研究 临床研究
背景情况:
- 失眠是常见的,但经常被诊断不足.
- 不一致的电子健康记录 (EHR) 文档阻碍了失眠研究.
- 准确的表型定型对于理解治疗模式和结果至关重要.
研究的目的:
- 开发和评估一个自动化系统的失眠表型使用生成型大语言模型 (LLMs).
- 改善在非结构化临床笔记中识别和记录失眠.
- 建立一个可扩展的框架,用于诊断不足的疾病的临床表型.
主要方法:
- 使用生成型大语言模型 (LLM),特别是Llama 70B和Llama 405B.
- 采用快速工程技术,使用短暂的学习和链式思维推理.
- 在两个不同的体上评估了该系统:MIMIC-III (住院) 和堪萨斯大学卫生系统 (KUMC) (门诊) 临床笔记.
主要成果:
- 获得了高的F1分数:在MIMIC体上达到93.0分,在KUMC体上达到85.7分.
- 与域调整的基于BERT的分类器相比,表现优越.
- 从非结构化临床文本直接成功表型失眠.
结论:
- 开发的基于LLM的框架为临床失眠表型提供了一个可扩展和可解释的解决方案.
- 这种方法可以增强对失眠患病率,治疗和结果的分析.
- 该方法作为在EHR中表型化其他未被诊断的疾病的模型.
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