Sets of Sub-Sequences based Sepsis Prediction for ICU Trauma Patients

Abstract

Sepsis is an extreme inflammatory response of the body to an infection. It is one of the leading causes of death in ICUs worldwide, resulting in approximately 25% mortality in critically ill populations. Early identification and intervention are crucial to reducing sepsis-associated mortality and improving patient prognosis because severe sepsis cases can lead to organ failure and other life-threatening complications. Diagnosis of Sepsis is challenging in terms of diagnostic accuracy and timeliness due to ambiguous symptoms and individual differences. In recent years, numerous efforts have been made using machine learning methods for sepsis prediction. However, there are still very limited successful implementations due to limitations in consistency of data input, which cannot fit the characteristics of uncertain time intervals and large number of missing values in real-world scenarios. In this study, we propose an innovative approach to predict sepsis occurrence in real-time, using a flexible graph structure to model patient health records, predicting future sepsis risk at any time after the first 48 hours using observations data from the past 12 hours. To the best of our knowledge, our proposed approach is the first ever implementation that uses a graph representation to overcome the problem of irregular input features and continuous risk prediction thereby improving the compatibility of the model in clinical settings. Experiments on multi-year longitudinal data from a large level-1 trauma center demonstrate the effectiveness of our approach.