Rx Strategist：使用LLM智能体系统进行处方验证
Rx Strategist: Prescription Verification using LLM Agents System
AI总结：
Rx Strategist通过知识图谱、多阶段LLM流水线和定制活性成分数据库实现处方验证，在适应症、剂量和药物相互作用检查上达到经验丰富临床药师水平，为减少处方错误提供新方案。
摘要：
为保障患者安全，现代制药的复杂性要求严格的处方验证。我们提出了一种新方法——Rx Strategist，该方法利用知识图谱和不同的搜索策略，在智能体框架内增强大型语言模型（LLMs）的能力。这种多层面的技术允许构建多阶段LLM流水线，并从定制构建的活性成分数据库中可靠地检索信息。处方验证的不同方面，如适应症、剂量和可能的药物相互作用，在流水线的每个阶段都得到覆盖。我们通过将推理分散到这些阶段来缓解单体LLM技术的缺点，在降低内存需求的同时提高正确性和可靠性。我们的研究结果表明，Rx Strategist超越了当前许多LLM，其性能可与经验丰富的临床药师相媲美。在现代药物复杂的环境中，将LLM与有组织的知识和先进的搜索方法相结合，为减少处方错误和改善患者预后提供了一条可行的途径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
