Machine learning to predict urine culture antibiotic sensitivities in the emergency department.

ABSTRACT:

Background

Urinary tract infections (UTIs) are among the most common bacterial infections diagnosed in the emergency department. Treatment of UTIs is largely empiric because urine culture results are not rapidly available.

Objectives

We examined whether machine learning could predict antibiotic sensitivities of the urine cultures by using only data available during the clinical encounter.

Methods

We used extreme gradient boosting (XGBoost) to examine 62,963 emergency department patient encounters from January 1, 2017, through December 31, 2021. All encounters included a urinalysis and urine culture. We included 1303 variables in the full model and examined 21 antibiotics. An antibiotic was characterized as sensitive only if all bacteria in the culture were susc

SUBMITTER: Sheele JM 

PROVIDER: S-EPMC11894297 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature