Temporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults.

Journal: Shock (Augusta, Ga.)
Published Date: Jul 1, 2021

Abstract

BACKGROUND: 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.

Authors

  • Akram Mohammed
    Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America.
  • Franco van Wyk
    University of Tennessee, Knoxville, TN, USA.
  • Lokesh K Chinthala
    Center for Biomedical Informatics-Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, 38103, USA.
  • Anahita Khojandi
    University of Tennessee, Knoxville, TN, USA.
  • Robert L Davis
    Center for Biomedical Informatics, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.
  • Craig M Coopersmith
    Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia.
  • Rishikesan Kamaleswaran
    Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA.

Keywords

External Resources

Popular Topics
Recent Journals