Real-Time Sepsis Prediction using an End-to-End Multi Task Gaussian Process RNN Classifier

Abstract

We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from infections that has high mortality and morbidity. Our proposed framework models the multivariate trajectories of continuous-valued physiological time series using multitask Gaussian processes, seamlessly accounting for the high uncertainty, frequent missingness, and irregular sampling rates typically associated with real clinical data. The Gaussian process is directly connected to a black-box classifier that predicts whether a patient encounter will become septic, chosen in our case to be a recurrent neural network to account for the extreme variability in the length of patient encounters. We show how several approximations scale the computations associated with the Gaussian process in a manner so that the entire system can be trained discriminatively end-to-end using backpropagation. In a large cohort of heterogeneous inpatient encounters at our university health system we find that it outperforms several baselines at predicting sepsis, and yields 33\% and 195\% improved areas under the Receiver Operating Characteristic and Precision Recall curves as compared to the NEWS score currently in use on our own hospital wards.