From EHRs to Patient Pathways: Scalable Modeling of Longitudinal Health Trajectories with LLMs
Journal:
arXiv
Published Date:
Jun 5, 2025
Abstract
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.
