arXiv Machine Learning August 4, 2026 By Chantal Pellegrini, Ege \"Ozsoy, David Bani-Harouni, Matthias Keicher, Nassir Navab

EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records

Read the original on arXiv Machine Learning →

arXiv:2506. 04831v3 Announce Type: replace Abstract: Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records

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