Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score
We developed eCARTv5, which performed better than eCARTv2, NEWS, and MEWS retrospectively, prospectively, and across a range of subgroups. These results served as the foundation for Food and Drug Administration clearance for its use in identifying deterioration in hospitalized ward patients.
Authors
- Brian W. Patterson (ORCID: https://orcid.org/0000-0002-4584-3808)
- Dana P. Edelson
- Nirav Shah (ORCID: https://orcid.org/0000-0002-9107-7788)
- Majid Afshar (ORCID: https://orcid.org/0000-0002-6368-4652)
- Christopher Winslow
- Kyle A. Carey (ORCID: https://orcid.org/0000-0002-5038-6074)
- Ashley Snyder (ORCID: https://orcid.org/0000-0002-1980-2092)
- Deborah J. Rhodes (ORCID: https://orcid.org/0000-0003-4408-6451)
- Matthew M. Churpek (ORCID: https://orcid.org/0000-0002-4030-5250)
- Alan Weiss
- Devendra Amin
- Emily Gilbert
Institutions
- Loyola University Medical Center (US)
- University of Wisconsin–Madison (US)
- Yale University (US)
- University of Chicago (US)
- BayCare Health System (US)
Publication Details
- Journal
- Critical Care Explorations
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1097/cce.0000000000001232
- Citations
- 5
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 3.27