Predicting urinary tract infections in the emergency department with machine learning.

ABSTRACT:

Background

Urinary tract infection (UTI) is a common emergency department (ED) diagnosis with reported high diagnostic error rates. Because a urine culture, part of the gold standard for diagnosis of UTI, is usually not available for 24-48 hours after an ED visit, diagnosis and treatment decisions are based on symptoms, physical findings, and other laboratory results, potentially leading to overutilization, antibiotic resistance, and delayed treatment. Previous research has demonstrated inadequate diagnostic performance for both individual laboratory tests and prediction tools.

Objective

Our aim, was to train, validate, and compare machine-learning based predictive models for UTI in a large diverse set of ED patients.

Methods

Single-center, multi-site, retrospective c

SUBMITTER: Taylor RA 

PROVIDER: S-EPMC5841824 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature