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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Errors-in-variables regression on ABIDE-I shows the IQ-motion slope is 4.67 times smaller than OLS estimates, and pooled models yield negative out-of-sample R-squared across all 19 sites.
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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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The IQ-Motion Confound in Multi-Site Autism fMRI May Be Inflated by Site-Correlated Measurement Uncertainty
Errors-in-variables regression on ABIDE-I shows the IQ-motion slope is 4.67 times smaller than OLS estimates, and pooled models yield negative out-of-sample R-squared across all 19 sites.