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EHRAgent:代码授权大型语言模型,用于电子健康记录上的短时间复杂的表格推理.

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EHRAgent是一个大型语言模型 (LLM) 工具,允许临床医生使用自然语言直接查询电子健康记录 (EHR). 该系统自主生成和执行代码,大大提高了访问复杂患者数据的效率.

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科学领域:

  • 人工智能的人工智能
  • 医疗信息学 医疗信息学
  • 临床数据管理 临床数据管理

背景情况:

  • 临床医生面临的挑战是访问电子健康记录 (EHR) 中复杂的患者数据.
  • 当前的数据检索流程依赖于数据工程师,这是低效和耗时的.
  • 需要与EHR系统进行直接的自然语言交互,用于临床决策.

研究的目的:

  • 推出EHRAgent,一个大型语言模型 (LLM) 代理,旨在与EHR系统自主交互.
  • 使临床医生能够使用自然语言查询检索复杂的患者信息.
  • 提高从多表式EHR数据中提取临床数据的效率和准确性.

主要方法:

  • 制定电子健康记录数据检索作为一个多表格推理和工具使用规划任务.
  • 开发具有累积领域知识和强大的编码能力的EHRAgent,用于自主代码生成和执行.
  • 注入相关的医疗信息,以增强EHRAgent对临床问题的推理.
  • 整合交互式编码和执行反,以实现代代码的改进.

主要成果:

  • 在解决复杂的临床任务方面,EHRAgent表现出强的表现.
  • 该系统成功地将复杂的查询分解为可管理的操作,使用外部工具集.
  • 在三个现实世界EHR数据集上的实验表明,EHRAgent在成功率上表现比最强的基线高达29.6%.
  • EHRAgent有效地从错误消息中学习,以代地改进代码生成.

结论:

  • EHRAgent使临床医生能够使用自然语言直接与EHR互动,减少对数据工程师的依赖.
  • 该LLM代理显示了提高临床数据检索和分析效率的巨大潜力.
  • EHRAgent能够自主生成,执行和改进代码的能力使其成为复杂临床任务的宝贵工具.