用角色分工的多智能体系统提升医疗预测，不依赖训练也能精准识别高危患者。
Role-Specialized Mixture-of-Agents with Open-Weight LLMs for Clinical Prediction
- 让不同角色的智能体分别负责知识检索和相似病人对比推理
- 在死亡率预测上达到与闭源模型相当的F1分数，且发现更多真实高危患者
- 角色设计直接影响召回率，适合注重隐私与无需训练的临床部署
大型语言模型（LLMs）被越来越多地用于从电子健康记录（EHRs）中进行住院死亡率和再入院等临床预测任务。由于隐私与合规限制，本地化部署的需求推动了开源权重多智能体系统的发展。然而，现有医学多智能体系统通常作为整体评估，难以判断各智能体角色对预测的贡献，也分不清检索环节是否带来实际性能提升。本文研究一种角色专业化混合智能体（MoA），结合医学知识检索与对比相似患者推理。通过固定检索设置而改变角色设计，发现主要效应来自最终集成器。将大型开源分析器与小型开源集成器搭配，在死亡率预测上的F1分数接近闭源模型，同时显著识别出更多真实高危患者。机制分析表明，角色分配可直接实现高召回率，无需阈值调整。该效果具有任务依赖性，再入院预测收益较小，因可用记录与长期结局相关性较弱。结果表明，在隐私受限、无需训练的临床预测中，角色设计是关键因素。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly applied to clinical prediction tasks such as in-hospital mortality and readmission from electronic health records (EHRs). Privacy and compliance constraints motivate systems that can be deployed locally, which has increased interest in open-weight multi-agent designs. However, most medical multi-agent systems are evaluated as a single block, leaving unclear which agent role contributes to prediction and whether retrieval drives observed gains. We study a role-specialized Mixture-of-Agents (MoA) that combines medical knowledge retrieval with contrastive similar-patient reasoning. By varying the role design while holding the retrieval setup fixed, we localize the main effect to the final integrator. Pairing large open-weight analysts with a small open-weight integrator matches closed-model prompting on F1 for mortality prediction while flagging substantially more true high-risk patients. Mechanism analysis shows the role assignment directly yields a high-recall operating point without threshold tuning. The effect is task-dependent, with smaller gains for readmission because the available records correlate weakly with this longer-horizon outcome. These results position role design as a key factor in privacy-constrained, training-free clinical LLM prediction.
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