Vol. 26 - Num. 102
Evidence based Pediatrics
Manuel Molina Ariasa, Eduardo Ortega Páezb
aServicio de Gastroenterología. Hospital Infantil Universitario La Paz. Madrid. España.
bPediatra. UGC Góngora. Distrito Granada-Metropolitano. Granada. España.
Correspondence: M Molina. E-mail: mma1961@gmail.com
Reference of this article: Molina Arias M, Ortega Páez E. Machine learning to identify febrile children at risk of Kawasaki disease . Rev Pediatr Aten Primaria. 2024;26:209-12. https://doi.org/10.60147/8be904ad
Published in Internet: 24-06-2024 - Visits: 2336
Abstract
Authors´ conclusions: the study suggests that the results of objective laboratory tests have the potential to predict Kawasaki disease. Machine learning with XGBoost can help clinicians differentiate Kawasaki disease patients from other febrile patients in pediatric emergency departments with excellent sensitivity, specificity, and accuracy.
Reviewers´ commentary: although the model presented has power to identify patients at risk of Kawasaki disease, it must be externally validated in populations more similar to ours before its use can be recommended.
Keywords
● Diagnostic techniques and procedures ● Machine learning ● Mucocutaneous lymph node syndromeNote:
Este artículo se publica simultáneamente con la revista electrónica Evidencias en Pediatría (www.evidenciasenpediatria.es).
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