使用大型语言模型从临床笔记中提取语言信息
Lingfei Qian1, Na Hong1, Yujia Zhou1
1Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA.
International journal of medical informatics
|September 24, 2025
概括
大型语言模型 (LLM) 可以准确地从临床笔记中提取患者的语言能力,改善医疗保健公平. 这个命名实体识别 (NER) 管道增强了通信和资源分配.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 患者的语言能力对于公平的护理和研究至关重要.
- 电子健康记录 (EHR) 的语言数据往往不完整或不准确.
- 异构的文档化做法阻碍了多机构的数据使用.
研究的目的:
- 使用大型语言模型 (LLM) 开发和评估命名实体识别 (NER) 管道.
- 准确地从非结构化的临床笔记中提取详细的患者语言状态.
- 为研究和实践提供可扩展和通用化的语言信息提取.
主要方法:
- 定义了四个语言状态类别:流利使用,部分能力,缺乏理解和不相关的提及.
- 来自耶鲁纽黑文医院 (YNHH) 和MIMIC-III的注释数据集.
- 评估专有 (GPT-4o) 和开源 (LLaMA3,BERT) LLM 在零射击和微调设置中.
- 评估跨站点通用性. 评估跨站点通用性.
主要成果:
- 在没有微调的情况下,GPT-4o获得了高F1分数 (87%YNHH,82%MIMIC).
- 微调的开源模型 (BERT,LLaMA3) 显示了同等或更高的性能.
- 与传统模型相比,LLM,特别是LLaMA3的跨机构通用性更强.
- 不相关的语言提及是最具挑战性的类别.
结论:
- 该NER框架准确地从临床叙述中提取细微的语言信息.
- 高准确度和通用性支持大规模的,以语言为重点的研究.
- 影响包括改善患者提供者沟通,口译服务和公平的医疗保健.
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