PatientWorld: A Physiology-Informed World Model for Learning Patient Dynamics

Predicting how a patient's physiology evolves over time under treatment is fundamental to clinical decision making. Longitudinal electronic health records (EHRs) provide large-scale physiological and treatment trajectories from which patient dynamics can be learned. However, many clinical trajectory models either focus on direct future-state prediction without explicitly modeling treatment-conditioned transitions or encode heterogeneous physiological variables into shared representations, leaving physiological system organization and cross-system dynamics implicit. We introduce PatientWorld, a physiology-informed world model for learning reusable treatment-conditioned patient dynamics. PatientWorld organizes latent states by physiological systems, explicitly models generic treatments and cross-system effects, uses physiological priors to structure the effects of physiology-specific treatments, and models state-dependent local transition residuals. During pretraining on broad ICU trajectories, it learns cross-system interactions among physiological systems; downstream, the pretrained dynamics remain fixed while task-specific context adaptation uses task-relevant physiological relations and action-conditioned residuals to specialize transition contexts. Across MIMIC-III and MIMIC-IV, PatientWorld outperforms compared methods in nearly all multivariate trajectory forecasting settings, improves downstream adaptation over alternative reuse strategies, and achieves the highest mean on the primary decision metric among the compared world models on both tasks.