From EHRs to Patient Pathways: Scalable Modeling of Longitudinal Health Trajectories with LLMs
This work addresses the problem of personalized healthcare prediction for patients and clinicians by enabling scalable modeling of complex health trajectories, though it appears incremental in building on existing pathway modeling approaches.
The paper tackles the challenge of modeling longitudinal patient health trajectories from electronic health records by proposing EHR2Path, a method that transforms EHR data into structured representations and uses a novel summary mechanism to embed long-term temporal context. The approach demonstrates strong performance in next time-step prediction and longitudinal simulation, outperforming competitive baselines.
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.