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), 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.