时间:用于纵向临床记录的时间指令建模和评估
Hejie Cui1, Alyssa Unell2, Bowen Chen3
1Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA.
NPJ digital medicine
|September 27, 2025
概括
TIMER 增强了用于分析电子健康记录 (EHR) 的大型语言模型 (LLM). 这种方法改善了LLM的时间推理,从而为临床决策提供了更好的患者时间线分析.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
背景情况:
- 电子健康记录 (EHR) 提供了宝贵的纵向患者数据.
- 目前的大型语言模型 (LLM) 面临着跨多次访问的EHR的时间推理方面的挑战.
研究的目的:
- 引入TIMER (纵向临床记录的时间指令建模和评估),这是一种提高LLM时间推理能力的新方法.
- 提高LLM在EHR中处理和解释患者时间表的能力.
主要方法:
- TIMER采用时间感知指令调,以在患者特定的时间背景下将LLM接地.
- 指令-响应对与特定的时间相连,以确保训练期间的时间忠实性.
主要成果:
- 与传统医疗指导调整模型相比,TIMER调整模型在临床医生策划的基准上完整性提高了6.6%.
- 使用TIMER进行分布匹配的训练,在时间推理方面显示出6.5%的优势.
- 定性分析证实了增强的时间边界坚持,趋势检测和时间精度.
结论:
- TIMER为开发熟练处理纵向EHR数据的LLM提供了方法论基础.
- 这种方法对于推进疾病轨迹建模和治疗反应监测等应用至关重要.
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