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

Blog Article

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 prediction of intravenous immunoglobulin resistance addressing the incompleteness of clinical data and the lack of interpretability of machine learning models, a multi-stage method is developed by integrating data missing pattern mining and intelligible models.First, co-clustering is adopted to characterize the block-wise data missing patterns by simultaneously grouping the clinical features and patients to enable Samsung RZ32M7120BC Free Standing 315 Litres A+ Upright Freezer Black (a) group-based feature selection and missing data imputation and (b) patient subgroup-specific predictive models considering the availability of data.

Second, feature selection is performed using the group Cake Turntables Lasso to uncover group-specific risk factors.Third, the Explainable Boosting Machine, which is an interpretable learning method based on generalized additive models, is applied for the prediction of each patient subgroup.The experiments using real-world Electronic Health Records demonstrate the superior performance of the proposed framework for predictive modeling compared with a set of benchmark methods.This study highlights the integration of co-clustering and supervised learning methods for incomplete clinical data mining, and promotes data-driven approaches to investigate predictors and effective algorithms for decision making in healthcare.