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arXiv AI By Chen Chen, Mohsen Nayebi Kerdabadi, Dongjie Wang, Mei Liu, Zijun Yao

Import What You Need: Learning When and How to Augment EHR Graphs with External Knowledge

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The paper introduces ReTA, a reinforcement‑learning framework that dynamically decides how to augment electronic health record (EHR) graphs with external knowledge graphs (KGs) on a per‑visit basis. ReTA offers three actions—Soft Import, Hard Import, and Skip—allowing it to enrich node features, graft compact KG subgraphs, or leave the graph unchanged depending on the patient’s evolving state and a budget constraint. Experiments on MIMIC‑III and MIMIC‑IV demonstrate that ReTA consistently outperforms strong baselines, transfers across datasets and KGs, and provides interpretable augmentation patterns while maintaining efficiency.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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