HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
For healthcare AI researchers, HypEHR offers an efficient alternative to costly LLM pipelines for EHR question answering by explicitly modeling hierarchical structure.
HypEHR introduces a compact hyperbolic embedding model for EHR question answering that leverages hierarchical clinical data structure, achieving performance close to LLM-based methods with significantly fewer parameters on MIMIC-IV benchmarks.
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 https://github.com/yuyuliu11037/HypEHR.