Machine Learning Approach to Predicting Absence of Serious Bacterial Infection at PICU Admission.

ABSTRACT:

Background and objectives

Serious bacterial infection (SBI) is common in the PICU. Antibiotics can mitigate associated morbidity and mortality but have associated adverse effects. Our objective is to develop machine learning models able to identify SBI-negative children and reduce unnecessary antibiotics.

Methods

We developed models to predict SBI-negative status at PICU admission using vital sign, laboratory, and demographic variables. Children 3-months to 18-years-old admitted to our PICU, between 2011 and 2020, were included if evaluated for infection within 24-hours, stratified by documented antibiotic exposure in the 48-hours prior. Area under the receiver operating characteristic curve (AUROC) was the primary model accuracy measure; secondarily, we calculated the numbe

SUBMITTER: Martin B 

PROVIDER: S-EPMC10350635 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature