使用大型语言模型实时检测药物诱导的肝损伤:从临床笔记的可行性研究
Thanathip Suenghataiphorn1, Pojsakorn Danpanichkul2, Narisara Tribuddharat3
1Department of Internal Medicine, Griffin Hospital, Derby, CT, United States.
Journal of clinical and experimental hepatology
|July 21, 2025
概括
大型语言模型 (LLM) 可以准确地从临床笔记中提取药物信息,用于早期药物诱导性肝损伤 (DILI) 监测. 该系统对实时DILI风险评估有希望,尽管需要进一步验证.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 药物监督 药物监督 药物监督
背景情况:
- 药物诱导性肝损伤 (DILI) 是一个重大的临床挑战,通常是晚发现的.
- 目前对DILI的监控方法缺乏实时功能.
- 电子医疗记录 (EMR) 为早期DILI检测提供了一个潜在的数据源.
研究的目的:
- 评估大型语言模型 (LLM) 驱动的实时DILI识别系统的技术可行性.
- 评估LLM能够从非结构化的临床笔记中提取药物数据以用于DILI监测的能力.
主要方法:
- 开发了一个LLM系统,从临床文本中提取药物清单,并进行代快速改进.
- 整合了DILIrank和LiverTox的DILI风险数据,使用LLM和算法匹配将药物联系起来.
- 与RxNORM,NHANES和现实世界的错误输入数据集对比,验证了提取的药物数据.
主要成果:
- 基于LLM的药物提取实现了高性能:精度0.96,回忆0.97,F1得分0.97%跨数据集.
- 在处理NHANES数据时没有发现任何错误.
- 可接受的F1得分分别为0.94和0.97,分别用于真实世界中的案例和错误输入的数据集.
结论:
- 从临床笔记中精确提取药物,这是实时DILI风险评估的关键步骤.
- 开发的系统需要在广泛实施之前进行进一步的临床验证和开发.
- 未来的研究将专注于提高匹配方法,临床验证,EMR集成,以及开发用于DILI风险分类的AI.
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