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MIMIC-IV

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

HCOE: hyperbolic ontology embeddings map BioBERT into a Poincaré ball for hierarchy-aware clinical concept representation

This arXiv v1 paper introduces Hyperbolic Clinical Ontology Embeddings (HCOE), which map frozen BioBERT embeddings into a Poincaré ball and train with parent-side and child-side ontology-guided contrastive learning plus coarse-to-fine ontology-path aggregation. HCOE is built using ICD codes organized by CCS and ATC medication hierarchies; evaluations report it outperforms baselines on ICD/ATC clinical relation prediction, CCS-to-PheCode hierarchy transfer, and downstream MIMIC‑IV tasks including mortality and readmission prediction, medication recommendation, and rare drug prediction.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

LearnActCoder (Learn-Then-Act): role-aware error memory boosts CPT coding F1 on MIMIC-III

arXiv:2609.19721v1 introduces Learn‑Then‑Act, an inference‑time adaptation framework that converts a small labeled LEARN batch into a structured Mistake Knowledge Database (MistakeKDB) and routes lessons to a recall‑oriented Coder or precision‑oriented Judge. Instantiated as LearnActCoder with lookup grounding, the method raises CPT/HCPCS F1 by 5.9 percentage points on 150 matched MIMIC‑III notes, shifts ICD‑10 coding toward higher precision on a MIMIC‑IV cohort (with recall loss), and preserves a stable ICD operating point on 1,000 held‑out MIMIC‑III notes; ICD‑9/ICD‑10 F1 changes are mixed and overall CPT/HCPCS absolute performance remains low; evaluation is retrospective.

5.0