Decision Support for Tactical Combat Casualty Care Using Machine Learning to Detect Shock.

Journal: Military medicine
PMID: 33499479

Abstract

INTRODUCTION: The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers' ability to treat patients create an urgent need for effective tools to support care. We report on a project to develop a phone-/tablet-based decision support system for prehospital tactical combat casualty care that collects physiologic and other clinical data and uses machine learning to detect and differentiate shock manifestation.

Authors

  • Christopher Nemeth
    Applied Research Associates, Albuquerque, NM 87110, USA.
  • Adam Amos-Binks
    Applied Research Associates, Albuquerque, NM 87110, USA.
  • Christie Burris
    Applied Research Associates, Albuquerque, NM 87110, USA.
  • Natalie Keeney
    Applied Research Associates, Albuquerque, NM 87110, USA.
  • Yuliya Pinevich
    The Mayo Clinic, Rochester, MN 55905, USA.
  • Brian W Pickering
    Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota, United States.
  • Gregory Rule
    Applied Research Associates, Albuquerque, NM 87110, USA.
  • Dawn Laufersweiler
    Applied Research Associates, Albuquerque, NM 87110, USA.
  • Vitaly Herasevich
    Department of Anesthesiology, Mayo Clinic, Rochester, MN, USA.
  • Mei G Sun
    US Army Medical Research & Development Command (USAMRDC), Fort Detrick, MD 21702, USA.

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