On Computer-Aided Prognosis of Septic Shock from Vital Signs

Abstract

Sepsis is a life-threatening condition due to the reaction to an infection. With certain changes in circulatory system, sepsis may progress to septic shock if it is left untreated. Therefore, early prognosis of septic shock may facilitate implementing correct treatment and prevent more serious complications. In this study, we assess the feasibility of applying a computer-aided prognosis system for septic shock. The system is envisaged as a tool to predict septic shock at the time of sepsis onset using only vital signs which are collected routinely in intensive care units (ICUs). To this end, we evaluate the performances of computational methods that take the sequence of vital signs acquired until sepsis onset as input and report the possibility of progressing to a septic shock before any further clinical analysis is performed. Results show that an adaptation of multivariate dynamic time warping can reveal higher accuracy than other known time-series classification methods on a new dataset built from a public ICU database. We argue that the use of computational intelligence methods can promote computer-aided prognosis of septic shock in hospitalized environment to a certain degree.