Bayesian Gaussian processes for identifying the deteriorating patient

Abstract

The step-down unit (SDU) is a high-acuity hospital environment, to which patients may be sent after discharge from the intensive care unit (ICU). About 1- in-7 patients will deteriorate in the SDU and require emergency readmission to the ICU. Upon readmission, these patients experience significantly higher mortality risks and lengths of stay. Gaussian process regression (GPR) models are proposed as a flexible, principled, probabilistic method to address the clinical need to monitor continuously patient time-series of vital signs acquired in the SDU. The proposed GPR models focus on the robust forecasting of patient heart rate time-series and on the early detection of patient deterioration. The proposed methods are tested with an SDU data set from the University of Pittsburgh Medical Center, comprising 333 patients, 59 of whom had at least one verified clinical emergency event. Results suggest that GPR-based heart rate monitoring provides superior advanced warning of deterioration compared to the current clinical practice of rules-based thresholding, and slightly outperforms the current state-of-the-art kernel density method, which requires 4 additional vital sign features.