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 of real-world data.To enable clinically applicable