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 th...
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| Main Authors: | , , , , , |
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| Format: | Book |
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Cambridge University Press,
2022-07-01T00:00:00Z.
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