Forecasting Patient Trajectories from the Electronic Health Records with Action-Conditioned World Models

We introduce a predictive world model for electronic health records (EHR) that represents patient histories as action conditioned latent trajectories. Rather than modeling clinical events solely through autoregressive token prediction, our framework explicitly separates patient states from observed