HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.
Oostrom, Djurre Holtrop, Zhaojie Luo, and Reinout E
2 Pith papers cite this work. Polarity classification is still indexing.
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Frozen multimodal embeddings with trait-specific late fusion cut personality prediction MSE by 19% relative to baseline in the 2026 AVI challenge, while cognitive results are attributed to validation shortcuts rather than content-based inference.
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HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.
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Frozen Multimodal Embeddings for AI-Assisted Interview Assessment of Personality and Cognitive Ability
Frozen multimodal embeddings with trait-specific late fusion cut personality prediction MSE by 19% relative to baseline in the 2026 AVI challenge, while cognitive results are attributed to validation shortcuts rather than content-based inference.