This paper is in the following e-collection/theme issue:

Artificial Intelligence (1660) Clinical Information and Decision Making (1601) Machine Learning (1680) Coronary Heart Disease (92) Congenital Heart Disease (8) Cardiac Diagnostics (20) New Diagnostic Tools and Instruments for Pediatrics (36) Screening and Early Diagnosis in Pediatrics (35) Computer-Aided Diagnosis (173) mHealth for Diagnosis (68) Digital Health Reviews (1472)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57641, first published .
Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis

Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis

Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis

Authors of this article:

Jinpu Zhu1 Author Orcid Image ;   Fushuang Yang2 Author Orcid Image ;   Yang Wang3 Author Orcid Image ;   Zhongtian Wang1 Author Orcid Image ;   Yao Xiao1 Author Orcid Image ;   Lie Wang2 Author Orcid Image ;   Liping Sun2 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Gu T, Pan W, Yu J, Ji G, Meng X, Wang Y, Li M. Mitigating bias in AI mortality predictions for minority populations: a transfer learning approach. BMC Medical Informatics and Decision Making 2025;25(1) View
  2. Mirata D, Tiezzi A, Buffoni L, Pagnini I, Maccora I, Marrani E, Mastrolia M, Simonini G, Giani T. Learning-Based Models for Predicting IVIG Resistance and Coronary Artery Lesions in Kawasaki Disease: A Review of Technical Aspects and Study Features. Pediatric Drugs 2025 View