The paper introduces Hyperbolic Clinical Ontology Embeddings (HCOE), a method that transforms frozen BioBERT embeddings into a Poincaré ball to capture medical code hierarchies. HCOE employs ontology-guided contrastive learning and coarse‑to‑fine ontology‑path aggregation, leveraging ICD, CCS, and ATC hierarchies. Experiments on MIMIC‑IV demonstrate superior performance in clinical relation prediction, hierarchy transfer, and various predictive tasks such as mortality, readmission, medication recommendation, and rare drug prediction.
By Yixuan Li, Weihao Li, Ziyang Song
arXiv:2609.15713v1 Announce Type: new
Abstract: Recent approaches to 30-day hospital readmission prediction rely on pre-trained language models applied to discharge summaries. Although these methods...
By Mohamad Najafi, Hongyun Fu, Mathias Brochhausen, Jian Wu, Yaohang Li
The paper introduces MEDIATOR, a discrete, ontology‑agnostic tokenizer that maps any clinical concept—whether encoded, synonymous, or free‑text—into a shared vector‑quantized codebook. By collapsing equivalent expressions and organizing distinct concepts in a medically meaningful space, MEDIATOR serves as the token interface for an EHR Transformer, boosting mean AUPRC by 4.0 points on 20 MIMIC‑III tasks and 8.6 points in zero‑shot transfer to 14 eICU tasks, while enabling full event coverage and transferable attribution across hospitals.
By Tong Ding, Caiwei Tian, Dongmin Bang, Ming Y. Lu, Sophia J. Wagner, Andrew Zhang, Rowland Pettit, Long Phi Le, Faisal Mahmood
The paper introduces UdonCare, a hierarchy‑pruning method that iteratively partitions patients into latent domains using medical ontologies, aiming to improve domain generalization in clinical prediction tasks. It addresses challenges of missing domain labels and lack of clinical insight by discovering hierarchy‑grounded patient domains. Experiments on MIMIC‑III, MIMIC‑IV, and eICU datasets show UdonCare outperforms eight baseline methods across four prediction tasks with significant domain gaps.
By Pengfei Hu, Xiaoxue Han, Fei Wang, Yue Ning
arXiv:2606. 15447v1 Announce Type: new Abstract: Electronic health record foundation models typically treat ICD diagnosis codes as flat tokens, overlooking the clinically meaningful hierarchical structure that captures disease families, subcategories, and fine-grained diagnostic detail.
By Megha Thukral, Dong Gyun Kang, Rudra Pratap Singh, Shruthi Kashinath Hiremath, Katrin H\"ansel, Thomas Pl\"otz
arXiv:2604. 09737v2 Announce Type: replace-cross Abstract: Structured prediction with large language models requires outputs that are label-accurate, ontology-constrained, structurally valid, and evidence-grounded under label imbalance and heterogeneous group difficulty.
By Samah Fodeh, Ganesh Puthiaraju, Elyas Irankhah, Afshan Khan, Sreeraj Ramachandran, Linhai Ma, Srivani Talakokkul, Sarah Schellhorn
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.
By Kaisong Zhang, Haotian Fang, Junmeng Zhou, Hang Lv, Yulan Pan, Yanchao Tan
arXiv:2608. 02692v1 Announce Type: new Abstract: Access to holistic, multimodal data improves the performance of Artificial Intelligence (AI) in medical classification tasks compared to utilizing single modalities or data sources.
By Julia Gehrmann, Lars Quakulinski, Hamza Naseem, Oya Beyan
REFINE is a framework that refines medical concept representations by creating patient‑specific temporal graphs from a global text‑attributed knowledge graph. It uses a reinforcement learning policy to allocate a personalized graph expansion budget for each observed code, then processes the resulting graph with a heterogeneous GNN and a frozen LLM that refines representations via graph‑aware soft prompts. Experiments on MIMIC‑III and MIMIC‑IV demonstrate that REFINE consistently improves various EHR prediction backbones, surpasses strong baselines, and shows robust gains across ablation studies, KG selection, and data insufficiency scenarios.
By Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Dongjie Wang, Zijun Yao
arXiv:2605.20197v2 Announce Type: replace
Abstract: Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaning...
By Zhichao Yang, Gregory D. Lyng, Sanjit Singh Batra, Robert E. Tillman
arXiv:2609.08174v1 Announce Type: new
Abstract: We introduce OntologyBench, a tiered biomedical retrieval benchmark comprising 471,854 training and 125,744 evaluation query-document relevance pairs a...
By Xiao Yu Cindy Zhang, Wyeth Wasserman, Jian Zhu
arXiv:2606. 02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs).
By Bo-Hong Wang, Baicheng Peng, Ruilin Wang, Jun Bai, Ziyang Song, Yue Li