Open AccessTemporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults
Author(s) -
Akram Mohammed,
Franco van Wyk,
Lokesh Chinthala,
Anahita Khojandi,
Robert L. Davis,
Craig M. Coopersmith,
Rishikesan Kamaleswaran
Publication year - 2020
Publication title -
shock
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.095
H-Index - 117
eISSN - 1540-0514
pISSN - 1073-2322
DOI - 10.1097/shk.0000000000001670
Subject(s) - sepsis , medicine , septic shock , receiver operating characteristic , intensive care unit , intensive care , critically ill , confidence interval , intensive care medicine , respiratory rate , emergency medicine , blood pressure , heart rate
Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our primary objective was to develop artificial intelligence capable of predicting sepsis earlier using a minimal set of streaming physiological data in real time.