Development of a machine learning-based prediction model for serious bacterial infections in febrile young infants.

ABSTRACT:

Background

To develop and validate machine learning (ML)-based models to predict serious bacterial infections (SBIs) in febrile infants aged ≤90 days.

Methods

This retrospective study analysed data from febrile infants (≥38.0℃) aged ≤90 days. The development dataset comprised data from patients who visited the Seoul Asan Medical Center between 2015 and 2021, whereas the validation dataset included data from those who visited the centre from January 2022 to August 2023. Logistic regression (LR) and eXtreme Gradient Boosting (XGB) were used to develop the models for predicting SBIs, which were then compared with traditional rule-based models.

Results

The study included data from 2860 patients: 2288 (80%) in the development dataset and 572 (20%) in the validation dataset

SUBMITTER: Park JS 

PROVIDER: S-EPMC12314954 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature