Jiang and colleagues conducted a retrospective study in cardiac surgery patients using machine learning models. Comparing logistic regression, random forest, and XGBoost, they found XGBoost excelled, with the anion gap as a crucial predictor. The study offers actionable insights for clinicians to adopt data-driven decision-making in patient care.

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Development and validation of machine learning models for predicting extubation failure in patients undergoing cardiac surgery: a retrospective study