A machine learning model to predict optimal antibiotic use in hospital medicine patients
We describe a model that predicts antibiotic necessity and optimal use using routinely available data that can be automatically aggregated from medical records. This makes it a promising option for early identification and intervention on orders most likely to benefit from ASP review.
Authors
- Priya A. Prasad (ORCID: https://orcid.org/0000-0002-1384-0960)
- Sandra Oreper (ORCID: https://orcid.org/0000-0003-1069-5646)
- Junghwan Lee (ORCID: https://orcid.org/0000-0002-1240-7655)
- Ripal Jariwala (ORCID: https://orcid.org/0000-0002-1377-908X)
- Allison Bond
- Nathan Radakovich (ORCID: https://orcid.org/0000-0001-5712-1952)
- Sarah B. Doernberg (ORCID: https://orcid.org/0000-0003-1727-6014)
- Xiao Hu (ORCID: https://orcid.org/0000-0001-9478-5571)
- Daniel Escobar
Institutions
- Georgia Institute of Technology (US)
- University of California System (US)
Publication Details
- Journal
- Antimicrobial Stewardship & Healthcare Epidemiology
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1017/ash.2025.10142
- Primary Topic
- Antibiotic Use and Resistance
- Type
- article
- Field-Weighted Citation Impact
- 0.00