Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease.

Journal: Italian journal of pediatrics
Published Date: Jun 9, 2025

Abstract

BACKGROUND: Kawasaki disease (KD) is a leading cause of acquired heart disease in children that is treated with intravenous immunoglobulin (IVIG). However, 10-20% of cases exhibit IVIG resistance, which increases the risk of coronary complications. Existing predictive models do not integrate multiple machine learning (ML) algorithms or facilitate real-time clinical use. This study presents a region-specific, interpretable ML model for early IVIG resistance prediction in KD.

Authors

  • Ying He
    Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University, and Medical School of Nantong University, Nantong, China.
  • Fan Lin
    Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xin Zheng
    Department of Clinical Laboratory, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China. Electronic address: dearjanna@126.com.
  • Qiaobin Chen
    Department of Pediatric, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, 350001, China.
  • Meng Xiao
    Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China.
  • Xiaoting Lin
    Department of Reproductive Medicine, The First Affiliated Hospital, Jinan University Guangzhou 510000, Guangdong, China.
  • Hongbiao Huang
    Department of Pediatric, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, 350001, China. 403032197@qq.com.

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