通过使用大型语言模型在电子健康记录中确定移动功能状态
Xingyi Liu1, Muskan Garg2, Heling Jia2
1Department of AI and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. liu.xingyi@mayo.edu.
Scientific reports
|January 23, 2026
概括
大型语言模型 (LLM) 可以从临床笔记中提取患者的移动状态. 这种方法通过标准化电子健康记录 (EHR) 的功能状态数据来支持精准医学.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 老年医学 老年医学
背景情况:
- 评估功能状态,特别是移动性,对于全球人口老龄化中的精准医学至关重要.
- 电子健康记录 (EHR) 包含有关患者流动性的宝贵非结构化数据.
- 从临床笔记中标准化移动状态是具有挑战性的,但对于临床应用和研究至关重要.
研究的目的:
- 调查大型语言模型 (LLM) 在从非结构化电子健康记录数据中提取和标准化患者移动状态的有效性.
- 评估不同的LLM提示策略,用于移动数据提取和损伤分类.
- 评估LLM衍生的流动性评估的临床可信性和通用性.
主要方法:
- 附注来自三个医疗机构的600份临床笔记,用于移动表达和障碍.
- 利用开源的Llama 3模型,提供零射击,少数射击和任务分解提示.
- 进行了错误分析和二次分析,认为"临床合理的推断"是正确的.
- 实施了本地,确定性设置,以提高可信度和隐私.
主要成果:
- 最终的LLM模型实现了高性能:F1得分为0.876的移动性提取和0.897的损伤分类.
- 考虑到临床上合理的推断,F1得分分别增加到0.962和0.948.
- 当地,决定性的设置确保了一致的输出,保护了隐私,并证明了跨机构的通用性.
结论:
- 基于LLM的解决方案可用于从非结构化的EHR数据中提取和标准化移动功能状态.
- 这项技术可以显著支持精准医学,临床决策和医疗保健研究.
- 这些发现强调了人工智能的潜力,可以从复杂的临床文本中获得宝贵的见解.
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