在观察性研究中使用大型语言模型进行病例裁决的标准化患者个人资料审查
Martijn J Schuemie1,2,3, Anna Ostropolets4,5, Aleh Zhuk4,6
1Observational Health Data Science and Informatics, New York, NY, USA. schuemie@ohdsi.org.
NPJ digital medicine
|January 9, 2025
概括
大型语言模型 (LLM) 显示出在观察性研究中自动化表型评估的前景,可能减少手动图表审查负担. 这项研究表明,LLM可以使用KEEPER系统有效地裁决患者案件.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 观测研究方法 观测研究方法
背景情况:
- 观察性研究经常使用行政索赔和电子健康记录,但数据限制带来了挑战.
- 在这些研究中验证表型算法通常需要劳动密集型的手动图表审查.
研究的目的:
- 调查使用大型语言模型 (LLM) 与知识增强电子配置文件审查 (KEEPER) 系统作为手动图表审查替代案例裁决的可行性.
- 评估LLM在评估患者病例识别的表型算法输出方面的表现.
主要方法:
- 该研究使用KEEPER系统从结构化数据中提取预定义的发现,用于通过表型算法识别的病例.
- 包括GPT-4在内的四个LLM被测试使用零射击提示和代提示优化来裁决案件.
- 在十种不同的疾病中评估了LLM的表现.
主要成果:
- 根据提示和模型,LLM的性能有所不同,灵敏度从78%到98%不等,特异性从48%到98%不等.
- 结果表明,LLM可以有效地评估KEEPER输出,以确定患者病例资格.
结论:
- 使用与KEEPER等系统集成的LLM自动化表型评估是观察性研究的一个有希望的方法.
- 这种方法提供了一个潜在的解决方案,以减少手动图表审查的负担,提高数据分析的效率.
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