Machine learning for personalized antimicrobial susceptibility breakpoints
Using machine learning models to predict the probability of complicated UTI in patients with bacteriuria and the probability of UTI in patients with bacteraemia resulted in appropriate aminopenicillin dosages/regimens being recommended in most cases. These results provide proof-of-concept for how machine learning could facilitate the personalized implementation of EUCAST aminopenicillin breakpoints.
Authors
- Conor Rosato (ORCID: https://orcid.org/0000-0001-8394-7344)
- Peter L. Green
- Iain Buchan (ORCID: https://orcid.org/0000-0003-3392-1650)
- Alessandro Gerada
- Yinzheng Zhong
- William Hope (ORCID: https://orcid.org/0000-0001-6187-878X)
- Anoop Velluva
- Alex Howard
Institutions
- University of Liverpool (GB)
- Liverpool College (GB)
Publication Details
- Journal
- Journal of Antimicrobial Chemotherapy
- Published
- 2025-11-12
- DOI
- https://doi.org/10.1093/jac/dkaf419
- Primary Topic
- Urinary Tract Infections Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Pfizer
- AstraZeneca
- Wellcome Trust
- UK Research and Innovation
- National Institute for Health and Care Research
- British Society for Antimicrobial Chemotherapy