Risk stratification through the model was specific when the hospital mortality was very low, low, moderate, high, and very high (2.0%, 10.2%, 11.5%, 21.2% and 56.2%, respectively). In external validation, the model had an area under the curve of 0.809 (95% CI 0.805–0.814). The performance of the machine learning model was superior to that of conventional risk predictive methods, with the area under curve 0.831 (95% CI 0.820–0.843) and acceptable calibration. The eICU Collaborative Research Database dataset was used for external validation. The logistic regression model and a common risk score for mortality were used for comparison. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. Using a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients’ clinical features and in-hospital mortality. Predicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units.
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