Sub-Sequence Graph Representation Learning on High Variability Data for Dynamic Risk Prediction in Critical Care

Ankur Teredesai, Sijin Huang, Tucker Stewart, Juhua Hu, Armaan Thakker, Katherine Stern, Grant E. O'Keefe

Abstract: Sepsis is an extreme inflammatory response of the body to an infection. It is one of the leading causes of death in critical care and 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 which are captured in heterogeneous varied data sources with irregular time sequences. In recent years, numerous efforts have been made using machine learning methods for sepsis prediction. However, there are still very limited successful industry scale implementations due to limitations of consistency of input data, which often does not fit the characteristics of uncertain time intervals and large number of missing values in real-world critical care settings. In this study, we demonstrate an innovative approach to predict sepsis occurrence in real-time, on heterogeneous sets of variables with multiple time-granularity, using a flexible graph structure to model patient health records. Our modeling task is to predict future sepsis risk at any time after the first 48 hours of admission using observations data from any time window within the past 12 hours. To the best of our knowledge, our proposed approach is the first ever implementation that is dynamic, uses a graph representation to overcome the problem of irregular input features, and offers continuous risk prediction, thereby improving the compatibility of the model in clinical settings. Unlike prior efforts that report results on de-identified curated public datasets with synthetic data filters, our experiments and results are validated by clinical experts, and are based on a large level-1 trauma center’s multi-year real-world longitudinal data.