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深度视野:通过对文本文档进行结构化,使用保护隐私的大型语言模型来增强内镜中围绕程序不良事件的记录
Isabella C Wiest1,2, Dyke Ferber2,3, Stefan Wittlinger1
1Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
iGIE : innovation, investigation and insights
|February 6, 2026
概括
大型语言模型 (LLM) 可以准确地从内镜报告中提取不良事件,改善质量控制. 符合隐私的Llama-2模型为GPT-4提供了可行的替代方案,用于可扩展的医疗数据分析.
科学领域:
- 医疗信息学医学信息学
- 自然语言处理自然语言处理.
- 内镜检查是指内镜检查.
背景情况:
- 在内镜手术中对不良事件的准确评估对于患者的安全和质量控制至关重要.
- 目前的自由文本文档阻碍了对干预结果的可扩展分析.
- 大型语言模型 (LLM) 为结构化不良事件提取提供了一个新的解决方案.
研究的目的:
- 评估LLM在自动从内镜报告中提取结构化不良事件数据方面的有效性.
- 为了比较GPT-4和Llama-2模型在识别出血,穿孔和吸入等不良事件的性能.
- 评估快速工程对医学文件的LLM绩效的影响.
主要方法:
- 使用OpenAI的GPT-4和Llama-2模型分析了672份内镜报告.
- 开发一个自动化的LLM管道用于向JavaScript对象符号中提取不良事件.
- 数据集分为即时工程的概念验证集 (n=171) 和外部测试集 (n=501).
主要成果:
- 在 PoC-S 中,GPT-4 显示出高精度 (例如97%的灵敏度,92%的特异性).
- 拉玛-2模型显示了可比的,符合隐私的性能 (例如,在PoC-S中94%的灵敏度,92%的特异性).
- 快速工程显著影响了模型的灵敏度和特异性.
结论:
- LLM提供了一种高效且可扩展的方法,用于从内镜报告中提取结构化的不良事件数据.
- 自动提取减少了手工劳动,并促进了即时的质量报告.
- 隐私意识的Llama-2模型为现实世界的实施提供了切实可行的替代方案.
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