Development of a machine learning model to diagnose pediatric lower respiratory tract infections.

ABSTRACT: This retrospective cohort study aimed to develop and validate a two-step machine learning (ML) approach for diagnosing pediatric community-acquired lower respiratory tract infections (CA-LRTIs) and distinguishing bacterial from viral pathogens to aid decision-making regarding antibiotic use. We employed five ML algorithms for model development and two testing sets for model evaluation. Patients < 19 years presenting with symptoms suggestive of CA-LRTIs between January 2005 and November 2023 were enrolled, excluding patients with tuberculosis, coronavirus-19 disease, or nosocomial infections. Initially, 9,329 patients were enrolled in the study; after applying 1:1 undersampling during data preprocessing, the final analytic cohort comprised 8,583 patients. Among the five ML algorithms, the r

SUBMITTER: Lee AR 

PROVIDER: S-EPMC12644617 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature