Evidence-based model for real-time surveillance of ARDS

Abstract

Real-time health surveillance becomes important and necessary with the increase of the elderly population to preserve their quality of life. Real-time models aim to provide alerts before the severe illness occurs. Acute respiratory distress syndrome is a crucial disease of the respiratory system that threats the health of the elderly. This paper proposes a real-time model for the surveillance of ARDS based on belief functions theory. Non-invasive physiological signals are considered such as heart rate, respiratory rate, oxygen saturation and mean airway blood pressure. Different linear and nonlinear parameters are extracted from these signals; then a parameters selection procedure is performed to reduce their dimensionality. Afterwards, classifiers are constructed using parameters distributions defined in the evidence framework. Real-time prediction is then performed by combining all classifiers decisions. As results, high performances are obtained over the testing sets with performances of 77% and 71% for sensitivity and specificity, respectively.