通过纳入临床实践指南来加强临床决策支持的大型语言模型
David Oniani1, Xizhi Wu1, Shyam Visweswaran1
1University of Pittsburgh, Pittsburgh, PA, USA.
概括
大型语言模型 (LLM) 在与临床实践指南 (CPG) 增强时,改善了临床决策支持 (CDS). 像二进制决策树 (BDT) 这样的方法对准确的COVID-19治疗建议有希望.
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
- 自然语言处理自然语言处理.
背景情况:
- 临床决策支持 (CDS) 系统对于基于证据的医疗保健至关重要.
- 将临床实践指南 (CPG) 整合到CDS的大型语言模型 (LLM) 中是一个尚未探索的领域.
- 对于CDS,现有的LLM方法缺乏系统的CPG集成.
研究的目的:
- 开发和评估用于将CPG纳入LLM的新方法,以加强CDS.
- 评估使用COVID-19门诊治疗作为案例研究的不同CPG整合策略的性能.
- 为了比较LLM的表现与没有CPG增强.
主要方法:
- 开发了三种CPG整合方法:二进制决策树 (BDT),程序辅助图形构造 (PAGC) 和思维链 - 几次拍摄促使 (CoT-FSP).
- 使用零射击提示 (ZSP) 作为基线.
- 通过自动和人体评估使用合成患者数据和四个LLM (GPT-4,GPT-3.5 Turbo,LLaMA,PaLM 2) 的评估方法.
主要成果:
- 与基线ZSP.相比,所有LLM在增强CPG时都显示出更好的性能.
- 在自动评估中,BDT比Cot-FSP和PAGC取得了更高的成绩.
- 所有拟议的CPG整合方法在人类评估中都显示出高效率.
结论:
- 与CPG增强的LLM显著提高了COVID-19门诊治疗临床决策支持的准确性.
- 开发的方法,特别是BDT,为将CPG整合到LLMs提供了有效的策略.
- 这些发现表明,CPG增强的LLM在各种临床决策场景中具有广泛的适用性.
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