Forecasting Patient Trajectories from the EHR with Action-Conditioned World Models

We introduce EHR-WM, a predictive world model for electronic health records that represents patient histories as action-conditioned latent trajectories. Rather than modeling clinical events solely through autoregressive token prediction, EHR-WM separates patient states from observed clinical actions and learns their evolution through a state- and action-conditioned transition operator. A state-dependent linearization predictor propagates latent changes over time, supporting multi-step rollout and action-conditioned simulation. On MIMIC-IV, generated trajectories preserve task-relevant and temporal structure, and decoded free-running rollouts remain predictive of future clinical measurements against task-matched forecasting baselines. Across classification and time-to-event tasks, EHR-WM is competitive but task-dependent relative to substantially larger EHR foundation models, using a 4.39M-parameter model over a restricted high-frequency feature space. Perturbing supplied action sequences produces corresponding changes in generated trajectories, demonstrating action-sensitive learned dynamics. These results support world modeling as a complementary framework for explicit, time-indexed modeling of patient evolution.