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

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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Bibliographic Details
Main Authors:	Ben Brintz, McKenna Nevers, Matthew Goetz, Kelly Echevarria, Karl Madaras-Kelly, Matthew Samore
Format:	Article
Language:	English
Published:	Cambridge University Press 2022-07-01
Series:	Antimicrobial Stewardship & Healthcare Epidemiology
Online Access:	https://www.cambridge.org/core/product/identifier/S2732494X22001905/type/journal_article

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