Citation

Babu, Tina and Nair, Rekha R. and Khoh, Wee How and Mogan, Jashila Nair and S., Sindhu (2026) Inherently Interpretable Kawasaki Disease Prediction Using Concept Bottleneck Models, Graph-Structured Concept Interactions, and Counterfactual Clinical Reasoning. Computers, 15 (9). p. 598. ISSN 2073-431X

Text computers-15-00598.pdf - Submitted Version Restricted to Repository staff only Download (5MB)

Abstract

Accurate differential diagnosis of Kawasaki disease (KD) from other febrile conditions remains challenging owing to nonspecific clinical presentation, absence of validated biomarkers, and reliance on post hoc explainability methods that cannot support clinician-level counterfactual reasoning. We propose ClinCBM-KD, an inherently interpretable concept bottleneck model routing all predictions through a clinically predefined 21-concept bottleneck aligned with AHA diagnostic criteria; the architecture integrates sparse concept encoders, a learnable concept interaction graph (CIG) modelling pairwise concept dependencies, and a concept-space gradient-boosting predictor, jointly optimised to enforce concept supervision, feature sparsity, and clinical graph priors. Evaluated on the Pediatric Heart Network registry (

Item Type:	Article
Uncontrolled Keywords:	Kawasaki disease, concept bottleneck model, explainable artificial intelligence, concept interaction graph, clinical decision support, paediatric diagnosis SHapley additive explanations, graph convolutional network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	28 Oct 2026 03:12
Last Modified:	28 Oct 2026 03:12
URII:	http://shdl.mmu.edu.my/id/eprint/16925

Downloads

Edit (login required)