Early Detection of Hypotension Using a Multivariate Machine Learning Approach
Abstract
The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action. Currently, there is limited research on combining multiple physiological signals for an early detection of hemorrhagic shock. We studied the viability of early detection of hypotension based on multiple physiologic signals and machine learning methods. We explored proof of concept with a small (5 minutes) prediction window for application of machine learning tools and multiple physiologic signals to detecting hypotension.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2021
- Source ID
- 10.1093/milmed/usaa323
Entities
People
- Jonathan T. Elliott
- Navid Rashedi
- Norman A Paradis
- Parikshit Shah
- Ryan Halter
- Vikrant Vaze
- Yifei Sun
Organizations
- Dartmouth College
- Geisel School of Medicine
- United States Department of Defense