一个LLM辅助的框架,用于从电子健康记录中加速和可验证的临床假设测试
medRxiv : the preprint server for health sciences
|February 23, 2026
概括
通过使用电子健康记录 (EHR),LATCH自动化临床假设测试. 这种人工智能框架加速了可重现的研究,并从现实世界的证据中产生了新的见解,减少了分析瓶.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的人工智能
- 临床研究信息学
背景情况:
- 电子健康记录 (EHR) 的手动分析阻碍了临床研究中的可扩展性和可重复性.
- 现有的工作流程在从复杂的EHR数据中提取可操作的见解时存在瓶.
研究的目的:
- 引入LATCH (临床假设LLM辅助测试),这是一个用于自动化对EHR数据进行临床假设测试的代理框架.
- 为了证明LATCH在复制,扩展和从已发表的研究中产生新见解的能力.
- 评估LATCH的性能,局限性和运营边界.
主要方法:
- 拉奇集成大型语言模型 (LLM) 辅助的语义层与确定性执行管道.
- 该框架自动化队列构建,统计分析和结果报告.
- 患者级数据与LLM涉及的步骤隔离,以确保隐私和安全.
主要成果:
- 在每项研究的3-15分钟内,LATCH重现了20项已发表的糖尿病研究的结果.
- 该框架使研究扩展和通过自然语言假设修改产生新的洞察力成为可能.
- LATCH成功执行了102个假设测试,证明了其在复制,扩展和洞察力生成方面的多功能性.
结论:
- LATCH提供了一个可扩展和可重复的框架,用于从电子健康记录中生成真实世界的证据.
- 该系统显著减少了分析瓶,并提高了人工智能辅助生物医学发现的可靠性.
- 在临床研究中,LATCH 保护了人类的监督,同时加快了发现过程.
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