Development and Validation of a Machine Learning Model for Early Detection of Untreated Infection
eDENTIFI could both decrease the time to antimicrobial administration in infected patients and unnecessary antibiotic administration in noninfected patients. Further prospective validation is needed.
Authors
- Jay Dumanian
- Nicole Dussault (ORCID: https://orcid.org/0000-0002-6423-3087)
- Dana P. Edelson
- Nirav Shah (ORCID: https://orcid.org/0000-0002-9107-7788)
- Kevin G. Buell (ORCID: https://orcid.org/0000-0001-5718-3328)
- Majid Afshar (ORCID: https://orcid.org/0000-0002-6368-4652)
- Christopher Winslow
- Kyle A. Carey (ORCID: https://orcid.org/0000-0002-5038-6074)
- Sivasubramanium V. Bhavani (ORCID: https://orcid.org/0000-0002-9640-6720)
- Matthew M. Churpek (ORCID: https://orcid.org/0000-0002-4030-5250)
- William F. Parker (ORCID: https://orcid.org/0000-0002-6000-3153)
- Emily Gilbert
Institutions
- University of Wisconsin–Madison (US)
- Emory University (US)
- Duke University (US)
- Loyola University Chicago (US)
- University of Chicago Medical Center (US)
- Duke Raleigh Hospital (US)
Publication Details
- Journal
- Critical Care Explorations
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1097/cce.0000000000001165
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institute of General Medical Sciences