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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2022-07-01
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| Series: | Antimicrobial Stewardship & Healthcare Epidemiology |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2732494X22001905/type/journal_article |