Early prediction of sepsis using a high-order Markov dynamic Bayesian network (HMDBN) classifier

Sepsis is among the leading causes of morbidity, mortality and high costs in the ICU. The early prediction and intervention of sepsis is a challenging task under strict time and cost constraints. In this paper, a novel High-order Markov Dynamic Bayesian Network (HMDBN) classifier with discrete features is presented for early prediction of sepsis at a high-order time point. The model structure is learned from the unrolled DBN by performing the K2 algorithm, and the features 'disappeared' in the prediction are eliminated using the VE method. Based on a few vital signs and laboratory results, an intuitive causal graph and indicating system are constructed to realize continuous prediction and probabilistic interpretation in real-time. Compared with other ten classical machine learning classifiers on evaluation metrics, HMDBN models have the highest AUROC scores on both internal tests and external validations for sepsis early prediction, and provide identifiable and interpretable results that allowing clinicians to immediately understand the reason for the prediction.