[2506.04831] EHR2Path: Scalable Modeling of Longitudinal Patient Pathways from Multimodal Electronic Health Records

Computer Science > Machine Learning arXiv:2506.04831 (cs) [Submitted on 5 Jun 2025 (v1), last revised 25 Mar 2026 (this version, v2)] Title:EHR2Path: Scalable Modeling of Longitudinal Patient Pathways from Multimodal Electronic Health Records Authors:Chantal Pellegrini, Ege Özsoy, David Bani-Harouni, Matthias Keicher, Nassir Navab View a PDF of the paper titled EHR2Path: Scalable Modeling of Longitudinal Patient Pathways from Multimodal Electronic Health Records, by Chantal Pellegrini and 4 other authors View PDF HTML (experimental) 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. 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 introduce EHR2Path, a multimodal framework for forecasting and simulating full in-hospital patient pathways from routine EHRs. EHR2Path converts diverse clinical inputs into a unified temporal representation, enabling modeling of a substantially broader set of patient information, including radiology reports, physician notes, vital signs, medication and laboratory patterns, and dense bedside charting. To support long clinical histories and broad feature spaces, ...