Researchers including Gensheng Zhang present a study detailing a 72-hour CatBoost model that uses 11 crucial variables and SHAP interpretations to predict in-hospital mortality among cardiac arrest patients. Using data from MIMIC-IV and external validations, this model offers a promising tool for risk stratification in ICUs. Consider its integration to refine timely clinical interventions.

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Development and validation of machine learning-based prediction model for outcome of cardiac arrest in intensive care units