Machine learning for the early prediction of sepsis patients in the intensive care unit (ICU) based on clinical data

Objective This study aimed to develop and validate machine learning models to predict 28-day mortality in sepsis patients admitted to the intensive care unit. Methods Initial clinical data from sepsis patients at the time of hospital admission including demographic characteristics, biochemical markers, infection sites, common comorbidities, and scoring systems were used to predict 28-day mortality of sepsis. Least absolute shrinkage and selection operator regression was applied to identify the most relevant predictive variables. After comparing seven algorithms-adaptive boosting, logistic regression, random forest (RF), K -nearest neighbors, Gaussian Naive Bayes, multilayer perceptron, and decision tree-we rebuilt the prediction model using the best-performing one. The model's performance was evaluated using multiple metrics, including the area under the curve (AUC), sensitivity, specificity, accuracy, positive and negative predictive values, F1 score, kappa statistic, and clinical decision curve analysis. Finally, the interpretability of the best-performing model was evaluated using the SHAP package. Results Seven critical features were screened including platelet distribution width to count ratio, mean platelet volume, serum creatinine, lactate, D-dimer, APACHE II score, and respiratory system infection. Among the seven algorithms, RF outperformed the others significantly. After training with the best-performing algorithm, the AUCs of the model in the training and validation sets were 1.0 and 0.933, respectively, and the model also performed well in the test set (AUC = 0.900, sensitivity = 0.742, specificity = 0.902, accuracy = 0.841, F1 score = 0.780). Conclusions A 28-day mortality in sepsis patients can be accurately predicted at an early stage using a machine learning model based on routinely collected clinical data.