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Learning a patient world model for ICU health system simulation

Researchers propose a neural network-based patient world model trained on longitudinal EHR data (MIMIC-IV) to simulate ICU operations across multiple policy questions. The model captures history-dependent dynamics that compact state models miss, such as a 24-hour recovery difference for patients with different prior trajectories. It matches Markov and semi-Markov comparators in aggregate occupancy and policy responses, while enabling scenario reconfiguration without model rebuilding.