发表机构
Optum AI; UnitedHealth Group（奥普姆人工智能部门; 联合健康集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对多模态纵向EHR的事件风险预测难题，提出结构化证据路由方法，经5项1年期诊断任务验证，其AUROC达到现有基线水平、AUPRC具竞争力，还可提供患者特定证据轨迹。
AI 中文摘要
从纵向电子健康记录（EHR）进行事件风险预测具有挑战性，因为相关信号是多模态的、单独来看较弱，且分布在不规则的患者病史中。我们提出结构化证据路由，这是一种路由器-预测器-审查器工作流程，将完整记录访问与特定疾病评估分离。路由器将完整的索引前EHR组织成紧凑摘要和目标证据片段；预测器利用这些证据形成与证据关联的风险评估，审查器则对其进行批评。为与有监督EHRSHOT基线进行比较，我们将路由的证据摘要与有监督分类器读出配对。在5项1年期事件诊断任务中，我们的方法达到了成熟有监督EHRSHOT基线的AUROC范围，且在AUPRC上保持竞争力，同时公开了患者特定的证据轨迹。内部读出前的消融实验进一步表明，路由、实验室证据、任务指导和审查均对性能有贡献。
英文摘要
Incident risk prediction from longitudinal electronic health records (EHRs) is challenging because relevant signals are multimodal, weak in isolation, and distributed across irregular patient histories. We propose structured evidence routing, a router-predictor-reviewer workflow that separates full-record access from disease-specific assessment. The router organizes the complete pre-index EHR into a compact summary and targeted evidence slices; the predictor uses this evidence to form an evidence-linked risk assessment, which the reviewer critiques. For comparison with supervised EHRSHOT baselines, we pair the routed evidence summaries with a supervised classifier readout. Across five 1-year incident diagnosis tasks, our method reaches the AUROC range of established supervised EHRSHOT baselines and remains competitive on AUPRC, while exposing a patient-specific evidence trail. Internal pre-readout ablations further suggest that routing, laboratory evidence, task guidance, and review each contribute to performance.
CommentsAccepted at the ICML 2026 Workshop on Structured Data for Health (SD4H), Seoul, South Korea
