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

Authors: Chantal Pellegrini, Ege Özsoy, David Bani-Harouni, Matthias Keicher, Nassir Navab

Healthcare systems face significant challenges in managing and interpreting vast, heterogeneous patient data for personalized care. Existing approaches often focus on narrow use cases with a limited feature space, overlooking the complex, longitudinal interactions needed for a holistic understanding of patient health. In this work, we propose a novel approach to patient pathway modeling by transforming diverse electronic health record (EHR) data into a structured representation and designing a holistic pathway prediction model, EHR2Path, optimized to predict future health trajectories. Further, we introduce a novel summary mechanism that embeds long-term temporal context into topic-specific summary tokens, improving performance over text-only models, while being much more token-efficient. EHR2Path demonstrates strong performance in both next time-step prediction and longitudinal simulation, outperforming competitive baselines. It enables detailed simulations of patient trajectories, inherently targeting diverse evaluation tasks, such as forecasting vital signs, lab test results, or length-of-stay, opening a path towards predictive and personalized healthcare.

Instructions

For detailed instructions on how to set up the environment, prepare the data, and train the models, please refer to the following readme files:

• Setup
• Dataset Generation
• Pathway Model Training and Evaluation
• Simulation Task Fine-tuning

Reference

@article{pellegrini2026ehrs,
  title={EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records},
  author={Pellegrini, Chantal and {\"O}zsoy, Ege and Bani-Harouni, David and Keicher, Matthias and Navab, Nassir},
  journal={Transactions on Machine Learning Research},
  year={2026}
}