利用大型语言模型从临床笔记中提取吸烟史,用于肺癌监测
Ingrid Luo1, Anna Graber-Naidich1, Mengrui Zhang1
1Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
NPJ digital medicine
|November 29, 2025
概括
大型语言模型 (LLM) 显著提高了电子健康记录 (EHR) 吸烟数据的准确性. 这提高了肺癌监测,通过更好地识别患者的风险和第二恶性瘤.
科学领域:
- 临床信息学 临床信息学
- 人工智能在医学中的应用
- 健康 数据科学 数据科学
背景情况:
- 在电子健康记录 (EHR) 中准确的吸烟记录对于患者风险评估和监测至关重要.
- 当前的电子健康记录数据往往缺乏或不准确的吸烟信息,限制了其实用性.
- 大型语言模型 (LLM) 提供了一种新的方法来解释临床文本并提取全面的吸烟数据.
研究的目的:
- 开发和评估一个使用LLM和基于规则的技术的框架,以提高电子健康报告中的吸烟数据的质量.
- 为了比较生成LLM与BERT模型的性能,以提取吸烟变量.
- 评估增强的吸烟数据对肺癌监测的影响.
主要方法:
- 开发了一个框架,将生成的LLM (Gemini-1.5-Flash,PaLM-2-Text-Bison,GPT-4) 与基于规则的纵向平滑相结合.
- 性能与基于BERT的模型进行了评估,使用来自518名患者的1683份手动注释的临床笔记.
- 对来自4792名肺癌患者的79,408张笔记进行了外部验证和大规模部署.
主要成果:
- 生成型LLM在提取七个吸烟变量时实现了超过96%的准确性,超过了基于BERT的模型.
- 外部验证证明了强大的概括性,准确度为97.5-98.8%.
- 基于风险模型的监测使用LLM提取的吸烟数据在识别肺癌患者的第二恶性瘤方面超过了NCCN指南.
结论:
- 生成型的LLM显示出显著的潜力,以提高电子健康记录中的吸烟史文档的质量和完整性.
- 改进的吸烟数据提取可以导致更有效的肺癌监测和风险分层.
- 这种方法对改善各种应用中的临床数据质量和患者护理具有广泛的影响.
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