从专家那里学习:一种自我改进的LLM框架,用于临床研究中研究人口的产生.
Yaoqian Sun1, Zikang Chen1, Hailing Cai1
1College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, 310027 Hangzhou, Zhejiang Province, China.
International journal of medical informatics
|November 6, 2025
概括
标准LLM是一个新的框架,使用大型语言模型 (LLM) 与临床医生反来从临床目标中生成可信的研究人群. 这种专家在循环中的方法增强了临床研究的真实世界证据生成.
科学领域:
- 临床研究中的人工智能
- 现实世界的数据 (RWD) 和现实世界的证据 (RWE) 生产.
- 临床研究设计优化
背景情况:
- 电子健康记录增加了现实世界数据 (RWD) 的可用性,用于现实世界证据 (RWE) 的生成.
- 大型语言模型 (LLM) 有助于RWD研究,但在可解释和可信的研究人口设计方面扎.
- 桥梁研究目标和下游分析RWD研究仍然是一个挑战.
研究的目的:
- 引入CriteriaLLM,一个框架,使LLM能够从临床研究目标中生成符合条件的研究群体.
- 纳入临床医生反和专家知识库,以加强LLM驱动的人口设计.
- 提高LLM产生的研究群体的可解释性和可信度.
主要方法:
- 标准LLM将临床医生的反集成到LLM中,用于研究人口的生成.
- 一个专家知识库,灵感来自后行动审查,记录LLM输出和临床医生修改.
- 一个双检索算法 (疾病领域相关性和词汇相似性) 使用历史案例指导LLM代.
- 一个连续的验证循环与代的专家反完善模型性能.
主要成果:
- 该框架在254个已发表的临床研究中使用MIMIC-III数据和四个LLM (GPT-4o,Deepseek-R1,LLaMA模型) 进行了评估.
- 在生成高质量的研究群体中,LLM 取得了高的 0.9180 的宏观 F1 评分.
- 该框架在不同尺寸和部署方法的LLM中展示了通用性.
结论:
- 标准LLM使LLM能够从临床目标中生成符合条件的研究群体,而无需微调.
- 结构化的专家反和检索指南提高了研究标准的质量和可靠性.
- 该框架提供了一个可扩展的,自我改进的方法,用于将AI整合到临床研究中,确保临床适当性和可解释性.
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