Comprehensive analyses: Using machine learning models for mortality prediction in the intensive care unit of internal medicine

Abstract

Mortality prediction in the intensive care unit (ICU) is essential in patient management. Emerging methods such as machine learning (ML) can be employed to predict ICU patients’ mortality. Patients receiving treatment in the ICU of the internal medicine department were subjected to ML analysis upon admission, considering demographic, laboratory, and medical scores. Data from 787 internal medicine ICU patients were analyzed, with only a subset (220) included in the study for the 30-day mortality prediction model. The performance of boosting and Logistic Regression models in mortality prediction was compared. Categorical boosting (CatBoost) achieved the highest area under the curve (AUC) of 0.90, while extreme gradient boosting reached a maximum AUC of 0.85, and Logistic Regression attained the highest AUC of 0.83. Incorporating Acute Physiology and Chronic Health Evaluation II, Simplified Acute Physiology Score II, and Sequential Organ Failure Assessment scores with clinical and laboratory values, CatBoost demonstrated the strongest predictive performance with high sensitivity and specificity. In the ICU of the internal medicine department, it was concluded that the ML models successfully predict mortality.