Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections.

ABSTRACT: Urinary tract infections are one of the most common bacterial infections worldwide; however, increasing antimicrobial resistance in bacterial pathogens is making it challenging for clinicians to correctly prescribe patients appropriate antibiotics. In this study, we present four interpretable machine learning-based decision support algorithms for predicting antimicrobial resistance. Using electronic health record data from a large cohort of patients diagnosed with potentially complicated UTIs, we demonstrate high predictability of antibiotic resistance across four antibiotics - nitrofurantoin, co-trimoxazole, ciprofloxacin, and levofloxacin. We additionally demonstrate the generalizability of our methods on a separate cohort of patients with uncomplicated UTIs, demonstrating that machine l

SUBMITTER: Yang J 

PROVIDER: S-EPMC11057209 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature