对结构化特征提取解剖学和临床病理学报告的大型语言模型的评估
Brody H Foy1, Kelly D Smith1, Olivia L Vargas1
1Department of Laboratory Medicine & Pathology, University of Washington Medicine, Seattle, WA, United States.
Clinical chemistry
|February 12, 2026
概括
大型语言模型 (LLM) 在从病理学笔记中提取结构特征方面表现出高准确性,为自动化临床数据分析提供了宝贵的工具. 这些LLM工具大大减少了与手动图表审查相关的时间和成本.
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.
- 计算病理学计算病理学
背景情况:
- 从临床笔记中手动提取特征是耗时且昂贵的.
- 自然语言处理 (NLP) 的进步为高通量特征提取提供了自动化解决方案.
- 大型语言模型 (LLM) 对病理学中的结构性特征提取有希望.
研究的目的:
- 从临床和解剖病理学笔记中评估LLM用于结构性特征提取的准确性.
- 将LLM绩效与不同病理学数据集的专家临床医生标签进行比较.
- 开发一个用于结构化函数快速原型化的工具,呼吁LLMs.
主要方法:
- 评估了OpenAI GPT-4o和GPT-5模型的心脏移植病理学,血红蛋白变体和尿液药物测试报告.
- 将LLM提取的特征与专家临床医生的手册标签进行了比较.
- 开发了一个网络应用程序 ("toolbuilder") 用于设计对LLMs的结构化函数调用.
主要成果:
- 法律法规的准确性很高,简单的错误率约为5%,复杂的错误率约为10%.
- 专家-LLM一致性很高 (κ>0.9),接近专家间的协议.
- 模型性能因类型而异,但对提示工程的敏感性较低;错误通常涉及将负值与不确定的结果区分开来.
结论:
- 基于LLM的工具可以自动化从临床笔记中提取时间和成本密集的特征.
- 这些自动化方法对临床护理和研究具有重要价值.
- 临床医学仪表显示了简化病理学数据注释和分析的潜力.
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