Enhanced Early Sepsis Onset Prediction: A Multi-Layer Approach

Abstract

Sepsis is a life-threatening organ malfunction caused by the host's inability to fight infection. Without proper and immediate treatment, sepsis can lead to death. Early diagnosis and medical treatment of sepsis in critically ill populations at high risk for sepsis and sepsis-associated mortality are vital to providing the patient with rapid therapy. The mortality rate increases with each hour that antibiotic treatment is delayed. Studies show that advancing sepsis detection by 6 hours leads to earlier administration of antibiotics, which is associated with improved mortality. However, clinical scores like SequentialOrgan Failure Assessment (SOFA) are not applicable for early sepsis onset prediction, while machine learning algorithms may be able to capture the progressing pattern for early prediction. Therefore, this thesis aims to develop a machine learning model that predicts sepsis onset 6 hours before it is suspected clinically. Although some machine learning algorithms have been applied to sepsis prediction, many of them did not consider the fact that six hours is not a small gap. To overcome this big gap challenge for early sepsis detection, this thesis explores a multi-layer approach in which the likelihood of sepsis occurring earlier than 6 hours is output from the 1st layer and fed to the 2nd layer as features to help predictions for the 6-hour horizon. Moreover, we use the hourly sampled data like vital signs in an observation window to derive a temporal change trend to further assist in sepsis prediction, which however is often ignored by previous traditional machine learning algorithms. Our empirical study shows that both the multi-layer approach to alleviating the 6-hour gap and the added features to capture the temporal trend can help improve the performance of early sepsis prediction.