Title: EHR2Path: Scalable Modeling of Longitudinal Health Trajectories with LLMs
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Electronic Health Records, Patient Pathway Modeling, Large Language Models

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. We will release our code upon acceptance.

TL;DR: We propose EHR2Path, a novel patient pathway foundation model that leverages efficient text and summary representations to predict future health trajectories.