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A machine-learning model using eight routinely available clinical variables accurately predicts vasoactive medication need after two fluid boluses in pediatric suspected sepsis, identifying children at risk for fluid-refractory septic shock.
Counterfactual simulation with a clinical world model means fixing a patient's history, changing the treatment, and reading off the predicted response. Doing so requires deciding what counts as one intervention. In clinical settings, interventions are documented as bundles: a co-occurrence audit of 945,707 patient-hour...
Clin-JEPA is presented, a five-phase curriculum that stably co-trains an LLM encoder with a latent trajectory predictor on MIMIC-IV, outperforming strong per-task tuned baselines and a pretrained EHR foundation model.
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