AN APPLICATION FOR EARLY PREDICTION OF PENDING SEPTIC SHOCK

摘要

The present invention is directed to a system and method for using physiological time-series (PTS) data sampled continuously from patients in the ICU. An algorithm according to an embodiment of the present invention applies statistical modeling and machine learning methods to implement an early warning policy for predicting those patients likely to transition from non-sepsis, early sepsis or sepsis into septic shock. Results demonstrate that the system and method of the present invention can provide higher sensitivity and specificity in this task than any other method reported to date. It provides an advanced early warning of this pending transition with median value 12.5 hours, giving ample opportunity for physicians to intervene to prevent the patient from developing septic shock.