From EHRs to Patient Pathways: Scalable Modeling of Longitudinal Health Trajectories with LLMs
From EHRs to Patient Pathways: Scalable Modeling of Longitudinal Health Trajectories with LLMs
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.
Chantal Pellegrini、Ege ?zsoy、David Bani-Harouni、Matthias Keicher、Nassir Navab
医学研究方法医学现状、医学发展
Chantal Pellegrini,Ege ?zsoy,David Bani-Harouni,Matthias Keicher,Nassir Navab.From EHRs to Patient Pathways: Scalable Modeling of Longitudinal Health Trajectories with LLMs[EB/OL].(2025-06-05)[2025-07-23].https://arxiv.org/abs/2506.04831.点此复制
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