用多智能体链模拟患者病程，提升肺癌风险预测准确性
Traj-CoA: Patient Trajectory Modeling via Chain-of-Agents for Lung Cancer Risk Prediction
- 构建智能体链条分步处理电子病历，逐段提炼关键事件
- 在五年病历上零样本预测一年内肺癌风险，表现优于四类基线
- 结果具临床合理性，适合医疗轨迹建模与风险预警场景
大型语言模型虽能通用建模患者病程，但受电子健康记录（EHR）数据长序列、高噪声影响，难以进行有效时间推理。为此，我们提出Traj-CoA，一种基于多智能体链的患者病程建模系统。Traj-CoA通过一系列工作者智能体依次处理EHR数据的片段，将关键事件提炼至共享的长期记忆模块EHRMem中，以降低噪声并保留完整时间线。最终由管理器智能体整合各工作者摘要与EHRMem中的时间线信息，完成预测。在基于五年EHR数据的零样本一年期肺癌风险预测任务中，Traj-CoA超越四类基准模型。分析表明，该方法具备符合临床逻辑的时间推理能力，展现出良好的鲁棒性与泛化潜力。代码已开源：https://github.com/zengsihang/Traj-CoA。
原文摘要 · Abstract (English)
Large language models (LLMs) offer a generalizable approach for modeling patient trajectories, but suffer from the long and noisy nature of electronic health records (EHR) data in temporal reasoning. To address these challenges, we introduce Traj-CoA, a multi-agent system involving chain-of-agents for patient trajectory modeling. Traj-CoA employs a chain of worker agents to process EHR data in manageable chunks sequentially, distilling critical events into a shared long-term memory module, EHRMem, to reduce noise and preserve a comprehensive timeline. A final manager agent synthesizes the worker agents' summary and the extracted timeline in EHRMem to make predictions. In a zero-shot one-year lung cancer risk prediction task based on five-year EHR data, Traj-CoA outperforms baselines of four categories. Analysis reveals that Traj-CoA exhibits clinically aligned temporal reasoning, establishing it as a promisingly robust and generalizable approach for modeling complex patient trajectories. Implementation of Traj-CoA is available on https://github.com/zengsihang/Traj-CoA.
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