External validation of predictive models for antibiotic susceptibility of urine culture
Machine learning algorithms trained on a large dataset are accurate in prediction of urine culture susceptibility vs resistance up to 3 days prior to urine AST availability. Clinical implementation of such an algorithm could improve both clinical care and antimicrobial stewardship.
Authors
- Sandip P. Vasavada (ORCID: https://orcid.org/0000-0002-8626-8248)
- Alex Milinovich (ORCID: https://orcid.org/0000-0003-0585-1480)
- Glenn T. Werneburg (ORCID: https://orcid.org/0000-0002-9518-672X)
- Daniel D. Rhoads (ORCID: https://orcid.org/0000-0002-7636-5191)
- Seán McSweeney (ORCID: https://orcid.org/0000-0002-3758-3161)
- Georges‐Pascal Haber (ORCID: https://orcid.org/0000-0002-7172-7050)
- Jacob Knorr
- Lyla Mourany
- Howard B. Goldman (ORCID: https://orcid.org/0000-0002-9782-1379)
- Alex Zajichek
Institutions
- Cleveland Clinic (US)
- Cleveland Clinic Lerner College of Medicine (US)
- Case Western Reserve University (US)
Publication Details
- Journal
- British Journal of Urology
- Published
- 2024-12-22
- DOI
- https://doi.org/10.1111/bju.16626
- Citations
- 3
- Primary Topic
- Bacterial Identification and Susceptibility Testing
- Type
- article
- Field-Weighted Citation Impact
- 1.14