使用大型语言模型驱动的多代理系统优化订单集

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Siru Liu^1,2, Sean S Huang^1,3, Allison B McCoy¹

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee.

JAMA network open

|September 23, 2025

概括

一个大型语言模型 (LLM) 驱动的多代理系统有效地优化了临床订单集. 这种人工智能方法通过提供可扩展的,与专家一致的建议来改善订单,从而增强决策支持和患者护理.

科学领域:

医疗保健中的人工智能
临床信息学临床信息学
卫生系统工程卫生系统工程

背景情况:

优化临床订单集对于提高决策支持和患者护理至关重要.
手动审查订单集是耗时的,并且在识别改进方面效率低下.
大型语言模型 (LLM) 提供了自动化和改进订单集优化的潜力.

研究的目的:

开发和评估一个大型语言模型 (LLM) 驱动的多代理系统,以优化临床订单集.
评估人工智能驱动的建议在提高订单设置准确性,实用性,可行性和影响方面的实用性和有效性.

主要方法:

开发了一个多代理系统,使用专门的代理来进行批评,搜索,知识检索,药物验证和总结.
实施了LLM-as-a-judge方法和过机制,以使AI建议与专家偏好和临床数据保持一致.
评估了范德比尔特大学医学中心的医生在71个订单组中产生的735个建议.

主要成果:

多代理系统产生了大量的建议,每次订单中位数为2个有用的建议.
专家对齐显著改善了人工智能建议的一致性,使科恩的 κ 从 0.06 增加到 0.41.
过减少了29%的建议总数,同时保留了92%被认为有用的建议.

结论:

基于LLM的多种代理系统为优化临床订单集提供了可扩展的解决方案.
将人工智能产生的建议与专家评级对齐,对于提高评估准确性至关重要.
未来的工作应该专注于改进AI推理,并将这些工具集成到电子健康记录中,以实时提供临床支持.

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