ArticleBMC pediatrics2025
Explainable deep learning algorithm for distinguishing IVIG-Resistant Kawasaki disease in Shandong peninsula, China.
Article in BMC pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 2 of them syntheses that pooled it.
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Who cites it
2 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Quality and performance of machine learning versus logistic regression for predicting IVIG resistance in Kawasaki disease: a PROBAST+AI systematic comparison.BMC medical research methodology · 2026Pooled it
- Machine learning prediction models for intravenous immunoglobulin resistance in Kawasaki disease: a meta-analysis.BMC medical informatics and decision making · 2026Pooled it
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntravenous immunoglobulin (IVIG) resistance of Kawasaki disease (KD) patients have a heightened risk of coronary artery lesions. We aimed to explore the predictive factors of IVIG resistance of KD from Shandong Peninsula in China, and established a explainable prediction model based on deep learning.
methodsThis retrospective cohort study include the patients and were divided into two subgroups: IVIG sensitive and IVIG resistance, then arbitrarily partitioned into training set and validation set at a ratio of 8:2. The data was then trained through support vector machines, Random forest, Decision tree, XGBoost, and LightGBM to determine the optimal model. Split and Gain were used to analyze the importance of variables.
resultsThis study included 914 KD patients (768 IVIG-sensitive and 146 IVIG-resistant). Comparative analysis revealed 19 laboratory indicators with significant differences by violin plots. The LightGBM displayed the highest comprehensive predictive capability trained with the above data, with area under curve (AUC) of 0.9936. Applying importance analysis (split and gain), 6 indicators were chosen, including C-reactive protein, Serum sodium, Albumin, Hemoglobin, Neutrophil percentage and Platelet. The LightGBM-Clinic model trained by the above indicators, with AUC of 0.9725.
conclusionsLightGBM can predict IVIG-resistance well in KD patients in Shandong Peninsula, China. C-reactive protein, Serum sodium, Albumin, HB, Neutrophil percentage, and PLT are important indicators, enhanced explainable of the model.
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