with a machine learning generated antibiogram.


Associations between β-lactams and other antimicrobial classes (macrolides, lincosamides, and fluoroquinolones) were common, although the strength of the association among these antimicrobial classes varied by infection site and by methicillin susceptibility. Association mining identified associations between clinically important AMR traits, which could be further investigated for evidence of resistance co-selection. For example, in skin and skin structure infections, clindamycin and tetracycline resistance occurred together 1.5 times more often than expected if they were independent from one another. Association mining efficiently discovered and quantified associations among resistance traits; allowing these associations to be compared between relevant subsets of isolates to identify and track clinically relevant MDR.

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