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arXiv AI By Kaisong Zhang, Haotian Fang, Junmeng Zhou, Hang Lv, Yulan Pan, Yanchao Tan

Coverage-Aware Reasoning with Medical Tokens for Diagnosis Prediction

Read the original on arXiv AI →

The paper introduces CARing, a framework that improves next‑visit diagnosis prediction by representing diagnoses with compositional Semantic IDs (SIDs) and optimizing multi‑label coverage through reinforcement learning. CARing encodes ontology‑enriched disease semantics into compact SIDs, aligns them with natural language and EHR contexts, and employs a coverage reward to encourage diverse diagnosis predictions. On MIMIC‑III and MIMIC‑IV datasets, CARing outperforms all EHR‑trained baselines in weighted F1 and achieves the highest top‑k recall, including R@30 scores above 46% in reasoning mode.

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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