Using machine learning to predict antibiotic resistance to support optimal empiric treatment of urinary tract infections

Background: Antibiotic resistance is pervasive in the Veterans’ Affairs (VA) healthcare system, with rates of fluoroquinolone and trimethoprim–sulfamethoxazole (TMP/SMX) resistance approaching 30% in E. coli urinary isolates. The efficacy of antimicrobial treatment is critically dependent on the sus...

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Detalles Bibliográficos
Autores principales: Brintz, Ben, Nevers, McKenna, Goetz, Matthew, Echevarria, Kelly, Madaras-Kelly, Karl, Samore, Matthew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2022
Materias:
Antibiotic Stewardship
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614875/
http://dx.doi.org/10.1017/ash.2022.190

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614875/
http://dx.doi.org/10.1017/ash.2022.190

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