Decision Support for Tactical Combat Casualty Care Using Machine Learning to Detect Shock
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2021
- Source ID
- 10.1093/milmed/usaa275
Entities
People
- Adam Amos-binks
- Brian W Pickering
- Christie Burris
- Christopher Nemeth
- Dawn Laufersweiler
- Gregory Rule
- Mei G. Sun
- Natalie Keeney
- Vitaly Herasevich
- Yuliya Pinevich
Organizations
- Applied Research Associates (United States)
- Mayo Clinic
- United States Army Medical Research and Development Command