一个面板并不适用于所有情况：面向临床预测的案例自适应多智能体协商
One Panel Does Not Fit All: Case-Adaptive Multi-Agent Deliberation for Clinical Prediction
AI总结：
本文提出CAMP框架，通过动态组建专家团队应对临床预测中的案例异质性，采用三值投票机制和混合路由策略，实现更精确的诊断预测和住院疗程生成，优于现有方法。
摘要：
应用于临床预测的大型语言模型表现出案例层面的异质性：简单案例产生一致输出，而复杂案例在微小提示变化下产生分歧预测。现有单智能体策略从单一角色条件分布采样，多智能体框架使用固定角色和扁平多数投票，丢弃分歧中的诊断信号。我们提出CAMP（案例自适应多智能体面板），其中主诊医师代理动态组建针对每个案例诊断不确定性的专家团队。每个专家通过三值投票（保持/拒绝/中立）评估候选方案，允许在专业领域外进行原则性弃权。混合路由器引导每个诊断通过强共识、回退至主诊医师判断或基于证据的仲裁，后者权衡论点质量而非投票数量。在MIMIC-IV数据集上，使用四个LLM基础架构进行诊断预测和短期住院疗程生成，CAMP在性能上始终优于强基线方法，同时消耗的token数量少于大多数竞争多智能体方法，投票记录和仲裁轨迹提供透明的决策审计。
英文摘要：
Large language models applied to clinical prediction exhibit case-level heterogeneity: simple cases yield consistent outputs, while complex cases produce divergent predictions under minor prompt changes. Existing single-agent strategies sample from one role-conditioned distribution, and multi-agent frameworks use fixed roles with flat majority voting, discarding the diagnostic signal in disagreement. We propose CAMP (Case-Adaptive Multi-agent Panel), where an attending-physician agent dynamically assembles a specialist panel tailored to each case's diagnostic uncertainty. Each specialist evaluates candidates via three-valued voting (KEEP/REFUSE/NEUTRAL), enabling principled abstention outside one's expertise. A hybrid router directs each diagnosis through strong consensus, fallback to the attending physician's judgment, or evidence-based arbitration that weighs argument quality over vote counts. On diagnostic prediction and brief hospital course generation from MIMIC-IV across four LLM backbones, CAMP consistently outperforms strong baselines while consuming fewer tokens than most competing multi-agent methods, with voting records and arbitration traces offering transparent decision audits.
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。
