发表机构
Virginia Tech（弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出角色专业化的开源权重智能体混合架构，结合医学知识检索与相似患者推理，在死亡率预测上媲美闭源模型且标记更多高危患者，为隐私约束下的临床LLM预测提供关键角色设计思路。
AI 中文摘要
大型语言模型（LLMs）正越来越多地被应用于临床预测任务，例如基于电子健康记录（EHR）的院内死亡率和再入院预测。隐私与合规约束促使系统可在本地部署，这提升了对开源权重多智能体设计的兴趣。然而，多数医疗多智能体系统是作为一个整体进行评估的，因此尚不明确哪个智能体角色对预测有贡献，以及检索是否推动了观测到的性能提升。我们研究了一种角色专业化智能体混合架构（MoA），它将医学知识检索与对比式相似患者推理相结合。在固定检索设置的前提下，通过改变角色设计，我们将主要效应定位于最终的整合器。将大型开源权重分析器与小型开源权重整合器配对，在死亡率预测的F1值上可媲美闭源模型的提示效果，同时标记出多得多的真正高危患者。机制分析显示，角色分配直接产生了高召回率的操作点，无需阈值调整。该效应具有任务依赖性，对于再入院预测的增益较小，因为可用记录与这一长期结果的相关性较弱。这些结果表明，角色设计是隐私约束下、无训练的临床LLM预测的关键因素。
英文摘要
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.
CommentsCOLM 2026 DAIH Workshop
