Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms.

ABSTRACT: In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study was to assess the utility of the most common machine-learning algorithms in the rapid triage of children with suspected COVID-19 using easily accessible and inexpensive laboratory parameters. A cross-sectional study was conducted on 566 children treated for respiratory diseases: 280 children with PCR-confirmed SARS-CoV-2 infection and 286 children with respiratory symptoms who were SARS-CoV-2 PCR-negative (control group). Six machine-learning algorithms, based on the blood laboratory data, were

SUBMITTER: Dobrijevic D 

PROVIDER: S-EPMC10383367 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature