We introduce ColaCare, a framework that enhances Electronic Health Record (EHR) modeling through multi-agent collaboration driven by Large Language Models (LLMs). Our approach seamlessly integrates domain-specific expert models with LLMs to bridge the gap between structured EHR data and text-based reasoning. Inspired by clinical consultations, ColaCare employs two types of agents: DoctorAgent and MetaAgent, which collaboratively analyze patient data. Expert models process and generate predictions from numerical EHR data, while LLM agents produce reasoning references and decision-making reports within the collaborative consultation framework. We additionally incorporate the Merck Manual of Diagnosis and Therapy (MSD) medical guideline within a retrieval-augmented generation (RAG) module for authoritative evidence support. Extensive experiments conducted on four distinct EHR datasets demonstrate ColaCare's superior performance in mortality prediction tasks, underscoring its potential to revolutionize clinical decision support systems and advance personalized precision medicine. The code, complete prompt templates, more case studies, etc. are publicly available at the anonymous link: https://colacare.netlify.app.

翻译：我们提出了ColaCare，这是一个通过大语言模型（LLMs）驱动的多智能体协作来增强电子健康记录（EHR）建模的框架。我们的方法将领域特定的专家模型与LLMs无缝集成，以弥合结构化EHR数据与基于文本的推理之间的差距。受临床会诊的启发，ColaCare采用了两种类型的智能体：DoctorAgent和MetaAgent，它们协作分析患者数据。专家模型处理数值型EHR数据并生成预测，而LLM智能体在协作会诊框架内生成推理参考和决策报告。我们还在一个检索增强生成（RAG）模块中整合了《默克诊疗手册》（MSD）医疗指南，以提供权威的证据支持。在四个不同的EHR数据集上进行的大量实验表明，ColaCare在死亡率预测任务中具有卓越的性能，凸显了其革新临床决策支持系统和推动个性化精准医学的潜力。代码、完整的提示模板、更多案例研究等已在匿名链接公开提供：https://colacare.netlify.app。