保护隐私的大型语言模型用于结构化医疗信息检索
Isabella Catharina Wiest1,2, Dyke Ferber2,3, Jiefu Zhu2
1Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
NPJ digital medicine
|September 20, 2024
概括
本研究介绍了一个开源管道,使用Llama 2大语言模型 (LLM) 来从自由文本中提取定量临床数据. 该LLM管道在识别脱补偿性肝硬化特征方面取得了很高的准确性,证明了其临床实用性.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 医疗保健中的人工智能
背景情况:
- 临床信息主要是非结构化的自由文本,限制了定量分析.
- 访问和分析自由文本临床数据对于改善患者护理和研究至关重要.
研究的目的:
- 开发和评估使用本地大语言模型 (LLM) 来从临床文本中提取定量信息的开源管道.
- 评估LLM在识别脱补偿性肝硬化症的关键特征方面的表现.
主要方法:
- 使用"Llama 2"大型语言模型 (LLM) 来从MIMIC IV数据集中的500个患者病史中提取信息.
- 采用零射击和一次射击的学习方法,具有不同的LLM大小和快速工程策略.
- 将LLM的预测与三位失明的医学专家建立的基本事实进行了比较.
主要成果:
- 在检测肝硬化时,LLM管道实现了高精度 (100%的灵敏度,96%的特异性).
- 还观察到高性能用于识别风 (95%, 95%),困惑 (76%, 94%),腹痛 (84%, 97%) 和呼吸短促 (87%, 97%).
- 与较小版本相比,70亿参数的LLM模型表现出优越的性能.
结论:
- 在本地部署的LLM可以有效地从自由文本中提取定量临床信息,而硬件要求最小.
- 这种方法提高了临床数据的可访问性,用于定量分析,并支持临床决策.
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