Integrating Co-Clustering and Interpretable Machine Learning for the Prediction of Intravenous Immunoglobulin Resistance in Kawasaki Disease

Integrating Co-Clustering and Interpretable Machine Learning for the Prediction of Intravenous Immunoglobulin Resistance in Kawasaki Disease

Identifying intravenous immunoglobulin-resistant patients is essential for the prompt and optimal treatment of Kawasaki disease, suggesting the need for effective risk assessment tools. Data-driven approaches have the potential to identify the high-risk individuals by capturing the complex patterns...

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Bibliographic Details
Main Authors: Haolin Wang, Zhilin Huang, Danfeng Zhang, Johan Arief, Tiewei Lyu, Jie Tian
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Co-clustering
interpretable machine learning
medical informatics
predictive models
Kawasaki disease
Online Access:https://ieeexplore.ieee.org/document/9097874/

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