Role-Specialized Mixture-of-Agents with Open-Weight LLMs for Clinical Prediction

2026-08-22

AI Summary

研究通过角色特化的多代理系统结合医学知识检索与对比相似患者推理，解决了临床预测任务中的隐私和合规性问题。

Abstract

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.

Problem

• Clinical Prediction
• Multi-Agent Systems
• Role Specialization
• Privacy Constraints
• Electronic Health Records

Innovation

• role-specialized Mixture-of-Agents (MoA)
• open-weight LLMs
• medical knowledge retrieval
• contrastive similar-patient reasoning
• high-recall operating point