Structured Evidence Routing for Incident Risk Prediction from Multimodal Longitudinal EHRs

Abstract: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.

Artificial Intelligence (cs.AI)

arXiv:2608.26191 [cs.AI]

(or arXiv:2608.26191v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.26191 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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