Medical World Regularizer: Learning from Clinical Interventions, Hidden States, and Future Trajectories

Medical AI systems predict diverse clinical endpoints from patient observations, but clinical care unfolds in a partially observed world shaped by interventions and revealed through subsequent measurements. A native task specifies what should be predicted; it need not specify which aspects of the evolving patient a representation should retain. Our MedWorldReg assigns longitudinal information three roles: past observations describe a state, prior interventions contextualize a decision, and later observations supervise what an earlier state should preserve. Intervention-aware decision making conditions the native readout on observed care. Future-aware state learning predicts representations of subsequent measurements from an earlier state. The two signals meet in a shared temporal encoder, while the task label, loss, and decision time remain unchanged. This task-world coupling uses no clinical simulator or counterfactual rollout, and its future-prediction branch is absent at deployment. The framework applies to longitudinal tasks with the requisite records, without presuming that every endpoint improves.