Early Prediction of Sepsis Based on Machine Learning Algorithm

Abstract

Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, aiming to predict early sepsis 6 hours in advance. The feature generation methods are constructed by combining different features, including statistical strength features, window features, and medical features. Miceforest multiple interpolation method is applied to tackle large missing data problems. Results show that the feature generation method outperforms the mean processing method. XGBoost and LightGBM algorithms are both excellent in prediction performance (AUC: 0.910∼0.979), among which LightGBM boasts a faster running speed and is stronger in generalization ability especially on multidimensional data, with AUC reaching 0.979 in the feature generation method. PTT, WBC, and platelets are the key risk factors to predict early sepsis.