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

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

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...

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Bibliographic Details
Main Authors: Ying He, Fan Lin, Xin Zheng, Qiaobin Chen, Meng Xiao, Xiaoting Lin, Hongbiao Huang
Format: Article
Language:English
Published: BMC 2025-06-01
Series:Italian Journal of Pediatrics
Subjects:
Kawasaki disease
Immunoglobulins
Intravenous
Prognosis
Machine learning
Internet-based intervention
Online Access:https://doi.org/10.1186/s13052-025-02036-1
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https://doi.org/10.1186/s13052-025-02036-1

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