药物组合警报的大型语言模型的基准测试:通过知识基础和上下文推理实现专家级可靠性
Huan Hu1,2, Liang Wang3, Li-Qun Chen1,2
1Institute of Applied Genomics, Fuzhou University, Fuzhou 350108, China.
Journal of medicinal chemistry
|January 20, 2026
概括
大型语言模型 (LLM) 在识别危险药物组合方面表现有前途. 增强的LLM与检索增强生成和专家推理实现药物相互作用警报的专家级准确性.
科学领域:
- 人工智能在医学中的应用
- 计算药理学计算药理学
- 临床决策支持系统 临床决策支持系统
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于医疗保健应用.
- 对于识别关键药物相互作用 (DDI) 的LLM的可靠性没有得到验证.
- 准确的DDI识别对于患者安全和有效的药物治疗至关重要.
研究的目的:
- 系统地评估使用CoMed框架对药物组合警报的LLM潜力.
- 评估知识基础 (RAG) 和专家推理对LLM绩效的影响.
- 证明多代理架构对于可解释的DDI风险分析的实用性.
主要方法:
- 评估本地LLM成绩作为基线.
- 集成检索-增强生成 (RAG) 用于外部知识接地.
- 应用上下文工程专家指导推理.
- 使用多代理架构进行全面的风险评估.
主要成果:
- 通过整合RAG和上下文工程,Qwen2.5-Max-CoT实现了高性能 (F1 = 0.971,AUC = 0.982).
- 该模型在DDI检测中展示了精度和回忆之间的专家级平衡.
- 一个关于阿司匹林-华法林的案例研究证实了CoMed框架能够生成准确,可追溯的HTML报告的能力.
结论:
- 增强的LLM,特别是RAG和专家推理,可以可靠地检测危险的药物组合.
- CoMed框架促进了透明和可追溯的药物相互作用风险评估.
- 这些先进的LLM显示出在药物治疗中支持临床决策的潜力.
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