在使用本地大语言模型的自由文本临床笔记中识别眼病进展的危险因素
medRxiv : the preprint server for health sciences
|November 19, 2025
概括
大型语言模型 (LLM) 从临床笔记中准确地识别了药物和访问不遵守,以及绿眼病的家族史. 这证明了LLMs.
科学领域:
- 眼科医生 眼科 眼科
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 电子健康记录 (EHR) 包含有价值的临床信息.
- 从非结构化的临床笔记中提取特定数据,如不遵守和家族病史是具有挑战性的.
- 青光眼家族病史 (FHoG) 是一个关键的风险因素,在电子健康记录中经常记录不准确.
研究的目的:
- 评估专业医疗大语言模型 (LLM) 在识别药物不服药,访问不服药和FHoG方面的表现.
- 为了将LLM绩效与FHoG的结构化EHR数据进行比较.
- 评估LLM能够量化眼的亲属数量的能力.
主要方法:
- 从电子健康记录 (2014-2024) 中提取了1,250个与眼相关的遭遇.
- 两位格劳科马专家手动标记了临床笔记,用于非坚持和FHoG.
- 使用MedGemma-27B-text-it LLM来处理笔记并识别目标信息.
- 计算准确度,灵敏度,特异性,雅卡德指数和平均平方误差 (MSE) 对于LLM性能.
主要成果:
- 在药物不服药 (0.91) 和访问不服药 (0.96) 方面,LLM取得了高准确度.
- 与结构化EHR字段相比,LLM在FHoG识别方面表现优异 (准确度:0.98,雅卡德指数:0.99) (准确度:0.49,雅卡德指数:0.75).
- 在量化患有青光眼的亲属中,LLM显著降低了MSE (0.05对0.85,p<0.001).
结论:
- 在临床笔记中,LLM可以准确地识别药物不服药,参观不服药以及FHoG的程度.
- 当地LLM管道提供了一个可扩展的解决方案,用于从非结构化EHR数据中研究青光眼风险因素.
- 简单的临床健康记录 (LLM) 增强了在离散的电子健康记录领域中不易获得的关键临床信息的提取.
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