通过自适应代自我查询检索来增强检索大型语言模型来增强临床决策支持
Srinivasagam Prabha1, Cesar A Gomez-Cabello1, Syed Ali Haider1
1Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA.
Bioengineering (Basel, Switzerland)
|August 28, 2025
概括
自我查询检索 (SQR) 框架通过自动结构和完善大型语言模型 (LLM) 的问题来改善临床决策支持. 这提高了人工智能产生的医疗指导的准确性和相关性.
科学领域:
- 医学的人工智能
- 临床决策支持系统
- 自然语言处理
背景情况:
- 使用大型语言模型 (LLM) 的检索增强生成 (RAG) 显示出临床指导的潜力,但受查询质量限制.
- 人工智能可以降低医生的认知负担, 但需要准确和结构化的信息检索.
- 目前的RAG系统与非结构化或模两可的临床查询作斗争,影响了可靠性.
研究的目的:
- 引入和评估自我查询检索 (SQR) 框架,以提高临床问题的清晰度和结构.
- 提高LLM产生的临床指导的准确性,相关性和检索质量.
- 在SQR框架内评估自动查询精细化模块 (PICOT,SPICE,IQR) 的有效性.
主要方法:
- 开发了自适应SQR框架,集成PICOT,SPICE和代查询精制 (IQR) 模块.
- 在Gemini-1.0 Pro LLM上实施了SQR,并与30个术后鼻整形查询进行了基准测试.
- 使用利克特尺度和检索指标 (精度,回忆,F1分) 评估响应的准确性和相关性.
主要成果:
- 完整的SQR管道实现了87%的准确性和100%的相关性,显著超过非精细的RAG基线 (50%的准确性,80%的相关性).
- SQR提高了精度,回忆力和F1分数,分别从0.17,0.39,0.24到0.53,1.00,0.70.
- 只有PICOT和SPICE的模块显示了中间改进,突出显示了整个管道的好处.
结论:
- 通过SQR进行自动查询结构和代改进,大大提高了基于LLM的临床决策支持.
- 在临床指导方面,SQR框架的准确性和相关性显著提高.
- SQR的模型无关设计使其在医学专业和数据源中广泛适用.
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