用知识图谱和分阶段搜索提升LLM的处方审核能力
Rx Strategist: Prescription Verification using LLM Agents System
- 构建多阶段LLM流水线，分步处理处方指征、剂量与相互作用
- 在真实处方数据上表现媲美资深临床药师，错误率显著降低
- 适合医疗AI、临床决策支持系统开发者参考
为保障患者安全，现代药物复杂性要求严格的处方审核。我们提出一种新方法——Rx Strategist，利用知识图谱与多种检索策略，在智能体框架中增强大语言模型（LLMs）的能力。该多阶段方法结合自建活性成分数据库，实现可靠信息检索，覆盖处方指征、剂量及潜在药物相互作用等关键环节。通过将推理过程分散到多个阶段，有效克服了单一模型的局限，提升了准确性和可靠性，同时降低了内存占用。实验结果表明，Rx Strategist在多项指标上超越当前多数LLM，性能接近资深临床药师水平。在现代药物体系下，该方法为减少处方错误、改善患者预后提供了可行路径。
原文摘要 · Abstract (English)
To protect patient safety, modern pharmaceutical complexity demands strict prescription verification. We offer a new approach - Rx Strategist - that makes use of knowledge graphs and different search strategies to enhance the power of Large Language Models (LLMs) inside an agentic framework. This multifaceted technique allows for a multi-stage LLM pipeline and reliable information retrieval from a custom-built active ingredient database. Different facets of prescription verification, such as indication, dose, and possible drug interactions, are covered in each stage of the pipeline. We alleviate the drawbacks of monolithic LLM techniques by spreading reasoning over these stages, improving correctness and reliability while reducing memory demands. Our findings demonstrate that Rx Strategist surpasses many current LLMs, achieving performance comparable to that of a highly experienced clinical pharmacist. In the complicated world of modern medications, this combination of LLMs with organized knowledge and sophisticated search methods presents a viable avenue for reducing prescription errors and enhancing patient outcomes.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。