Learning Continuous Patient Trajectories from Electronic Health Records

2026-09-28

AI Summary

This study addresses the challenge of irregular observations in electronic health records (EHRs), which complicates continuous patient state modeling and history-dependent prediction. To overcome this, we propose EHRFlow, a framework that extends multi-marginal flow matching to a conditional generative paradigm for the first time. By integrating Transformer-based medical history encoding, EHRFlow enables future dynamics to explicitly depend on prior clinical trajectories, facilitating continuous state prediction at arbitrary time points. Evaluated on million-scale real-world datasets, our approach significantly improves Top-5 code prediction accuracy. Furthermore, through counterfactual simulation, it effectively estimates intervention effects, providing a reliable tool for precision medicine.

Abstract

Electronic health records provide irregular observations of latent patient states that evolve continuously over time. Recent autoregressive models condition on clinical histories to forecast future events as sequences of discrete observations. Conversely, multi-marginal flow matching provides a continuous-time formulation, but using multiple observations to supervise training paths does not itself give the learned dynamics access to preceding patient history. We introduce EHRFlow, a multi-marginal flow-matching framework that conditions on encoded patient history, thereby allowing future dynamics to depend on the patient's prior clinical trajectory. Our proposed framework accommodates irregular observation times and supports forecasting at arbitrary horizons. Across controlled synthetic benchmarks, EHRFlow improves clinical-code forecasting and latent-state recovery. On real-world clinical datasets comprising more than one million patients, including an independent external validation cohort, EHRFlow improves horizon-averaged top-5 clinical-code accuracy over autoregressive and history-independent flow-matching baselines. Finally, in a controlled counterfactual simulation, conditional guidance approximates the known effect of an antihypertensive intervention without training a task-specific outcome model.

Problem

• Electronic Health Records
• Patient Trajectories
• Continuous-time Modeling
• Clinical Forecasting
• Irregular Observations

Innovation

• multi-marginal flow matching
• electronic health records
• continuous patient trajectories
• irregular observations
• counterfactual simulation