Use of Machine Learning to Differentiate Children With Kawasaki Disease From Other Febrile Children in a Pediatric Emergency Department.

ABSTRACT:

Importance

Early awareness of Kawasaki disease (KD) helps physicians administer appropriate therapy to prevent acquired heart disease in children. However, diagnosing KD is challenging and relies largely on subjective diagnosis criteria.

Objective

To develop a prediction model using machine learning with objective parameters to differentiate children with KD from other febrile children.

Design, setting, and participants

This diagnostic study included 74 641 febrile children younger than 5 years who were recruited from 4 hospitals, including 2 medical centers and 2 regional hospitals, between January 1, 2010, and December 31, 2019. Statistical analysis was performed from October 2021 to February 2023.

Main outcomes and measures

Demographic data and laboratory va

SUBMITTER: Tsai CM 

PROVIDER: S-EPMC10091152 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature