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

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;27(4):465 View
  3. Moncur C, Kamotho M, Jain T, Weslock N, Ragheb M, Mitchell K. Early life exposure to ambient particulate matter and Kawasaki disease: a systematic review and meta-analysis. Frontiers in Cardiovascular Medicine 2025;12 View
  4. Martel M, José-Garcia A, Vens C, De Vos M, Sobanski V. Artificial intelligence for precision medicine. Therapies 2025 View
  5. Xue X, Dai G, Lin Y, He L, Sheng W, Sun J, Liu F. Ferroptosis-related oxidative stress activation in the acute phase of Kawasaki disease. Frontiers in Immunology 2025;16 View