Early prediction of multiple organ failure for sepsis patients based on machine learning algorithms

Abstract In recent years, there has been a notable rise in sepsis incidence leading to more multiple organ failure and higher mortality. The lack of effective treatments for sepsis highlights the importance of early prediction in preventing multiple organ failure. This study aimed to develop a model for the early prediction of multiple organ failure in sepsis patients facilitating timely intervention by healthcare professionals. We extracted data of 2720 sepsis patients from Medical Information Mart for Intensive Care III database and used support vector machine (SVM), logistic regression (LR), random forest (RF), and eXtreme Gradient Boosting algorithms to predict heart, kidney, liver, and respiratory failure. Our models demonstrated strong predictive performance. LR performed best in predicting heart failure and kidney failure with area under the curve (AUC) values of 0.95 and 0.87, respectively. SVM and RF showed good performance in predicting liver failure (AUC = 0.93) and respiratory failure (AUC = 0.87), respectively. Furthermore, we conducted an importance ranking to identify the most significant features for predicting organ failure. The results demonstrated all four models effectively predicted multiple organ failure in sepsis patients. The study highlights machine learning's value as an early prediction tool and clarifies links between organ failure types and physiological parameters.