Machine learning to predict bacteriuria in the emergency department.

ABSTRACT: Urinary tract infections (UTIs) are among the most common bacterial infections, yet they are both frequently misdiagnosed and inappropriately treated. We aimed to determine whether a machine learning model could accurately predict bacteriuria by using only the data that are readily available during the emergency department (ED) patient encounter. We retrospectively identified records of 62,963 patient encounters at our EDs that included results from a urinalysis and urine cultures. Encounters occurred from January 1, 2017, through December 31, 2021. We used a logistic regression classifier, k-nearest neighbors, random forest classifier, extreme gradient boosting (XGBoost), and a deep neural network to determine how well they predicted 3 urine culture outcomes: (1) no microbial growth vs. a

SUBMITTER: Sheele JM 

PROVIDER: S-EPMC12375733 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature