评估急性心力衰竭分类的大型语言模型,并从法国临床笔记中提取信息
Adrien Bazoge1, Matthieu Wargny2, Pacôme Constant Dit Beaufils3
1Nantes Université, CHU Nantes, Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique des données, INSERM, CIC 1413, F-44000, Nantes, France; Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France.
Computers in biology and medicine
|June 20, 2025
概括
大型语言模型 (LLM) 可以从临床笔记中识别急性心力衰竭 (AHF) 住院. 一个专门的法国生物医学模型超过了一般的LLM,尽管一般模型在提取特定的定量数据方面表现出色.
科学领域:
- 人工智能在医学中的应用
- 医疗保健的自然语言处理.
- 临床数据提取
背景情况:
- 由于依赖于非结构化的电子健康记录 (EHR) 数据,急性心力衰竭 (AHF) 的诊断具有挑战性.
- 从自由文本注释中提取准确的临床信息对于理解AHF至关重要.
研究的目的:
- 评估大型语言模型 (LLM) 以自动识别AHF住院情况并提取临床细节.
- 将一个通用LLM (Qwen2-7B) 与一个法国生物医学LLM (DrLongformer) 的性能进行比较.
主要方法:
- 利用南特大学医院的临床笔记进行模型培训和评估.
- 员工监督微调和上下文学习 (几次拍摄,思维链提示).
- 进行了废弃研究,以评估数据量和注释特征的影响.
主要成果:
- 与Qwen2-7B (F1=0.80) 相比,DrLongformer在AHF分类 (F1=0.878) 和大多数信息提取任务中取得了更高的性能.
- 在微调后,Qwen2-7B在提取定量结果 (例如体重,BMI) 中表现出卓越的性能.
- 模型性能与培训数据量有显著的相关性,在250份文档后回报率下降;较长的注释是有害的.
结论:
- 生物医学特定的LLM显示出对准确的AHF识别和从EHR中提取数据的巨大潜力.
- 一般用途的LLM可能是有效的,特别是用于特定的定量数据提取,并进行微调.
- 可在本地部署的小型语言模型为改善医院系统内临床数据收集和AHF症状识别提供了可行的解决方案.
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