Developing Machine-Learning Models to Predict Bacteremia in Febrile Adults Presenting to the Emergency Department: A Retrospective Cohort Study from a Large Center
Our machine-learning model has shown excellent discriminatory performance to predict bacteremia based only on clinical features at ED triage. It has the potential to improve care quality and save more lives if successfully implemented in the ED.
Authors
- Chun-Min Fu (ORCID: https://orcid.org/0000-0001-7617-9498)
- Chien‐Yu Lin (ORCID: https://orcid.org/0000-0002-5263-8730)
- Tsung‐Chien Lu (ORCID: https://orcid.org/0000-0003-2729-7142)
- Shey‐Ying Chen (ORCID: https://orcid.org/0000-0002-5855-6170)
- Phyllis Lau (ORCID: https://orcid.org/0000-0002-0665-6348)
- Chu‐Lin Tsai (ORCID: https://orcid.org/0000-0003-4639-1513)
- Wei Hou (ORCID: https://orcid.org/0000-0002-3497-4378)
- Chih‐Hung Wang (ORCID: https://orcid.org/0000-0001-5058-4356)
- Fan‐Ya Chou
- Ike Ngo
- Yaroslav Ivanchuk
Institutions
- Fu Jen Catholic University (TW)
- National Taiwan University (TW)
Publication Details
- Journal
- Western Journal of Emergency Medicine
- Published
- 2025-05-30
- DOI
- https://doi.org/10.5811/westjem.35866
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00