AI RESEARCH
EHR2Path: Scalable Modeling of Longitudinal Patient Pathways from Multimodal Electronic Health Records
arXiv CS.LG
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ArXi:2506.04831v2 Announce Type: replace Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data. Yet, existing approaches typically focus on isolated prediction tasks, narrow feature spaces, or short context windows, limiting their ability to model full patient pathways. To address this gap, we.