HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering

Computer Science > Artificial Intelligence [Submitted on 22 Apr 2026] HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering Yuyu Liu, Sarang Rajendra Patil, Mengjia Xu, Tengfei Ma Electronic health record (EHR) question answering is often handled by LLM-based pipelines that are costly to deploy and do not explicitly leverage the hierarchical structure of clinical data. Motivated by evidence that medical ontologies and patient trajectories exhibit hyperbolic geometry, we propose HypEHR, a compact Lorentzian model that embeds codes, visits, and questions in hyperbolic space and answers queries via geometry-consistent cross-attention with type-specific pointer heads. HypEHR is pretrained with next-visit diagnosis prediction and hierarchy-aware regularization to align representations with the ICD ontology. On two MIMIC-IV-based EHR-QA benchmarks, HypEHR approaches LLM-based methods while using far fewer parameters. Our code is publicly available at this https URL. Comments: Accepted by Findings of ACL 2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.21027 [cs.AI]   (or arXiv:2604.21027v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.21027 Focus to learn more Submission history From: Yuyu Liu [view email] [v1] Wed, 22 Apr 2026 19:18:36 UTC (5,633 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)