Validation of a Machine Learning Model for Early Shock Detection.

Journal: Military medicine
PMID: 34056656

Abstract

OBJECTIVES: The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity, and other estimates of diagnostic performance compared to the gold standard of electronic medical records (EMRs) review.

Authors

  • Yuliya Pinevich
    The Mayo Clinic, Rochester, MN 55905, USA.
  • Adam Amos-Binks
    Applied Research Associates, Albuquerque, NM 87110, USA.
  • Christie S Burris
    Applied Research Associates, Albuquerque, NM 87110, USA.
  • Gregory Rule
    Applied Research Associates, Albuquerque, NM 87110, USA.
  • Marija Bogojevic
    Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA.
  • Isaac Flint
    Applied Research Associates, Albuquerque, NM 87110, USA.
  • Brian W Pickering
    Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota, United States.
  • Christopher P Nemeth
    Applied Research Associates, Albuquerque, NM 87110, USA.
  • Vitaly Herasevich
    Department of Anesthesiology, Mayo Clinic, Rochester, MN, USA.

Keywords

External Resources

Popular Topics
Recent Journals