Inherently Interpretable Kawasaki Disease Prediction Using Concept Bottleneck Models, Graph-Structured Concept Interactions, and Counterfactual Clinical Reasoning

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 (n=645; 77 analytical variables) via a stratified 80/20 training, held-out test split with ten-fold cross-validation within the training partition, ClinCBM-KD achieved an AUC-ROC of 0.908, accuracy of 87.3%, recall of 91.8%, and F1-score of 90.0%, with no statistically significant AUC difference versus the strongest post hoc baseline (logistic regression, AUC 0.910; DeLong p=0.741), concept completeness Γ=0.921, soundness Ω=0.884, and the lowest Brier score (0.094) of all classifiers; ablation confirmed concept supervision as the dominant contributor (ΔAUC=0.040). A temporally restricted evaluation retaining only c1–c17 (excluding treatment-derived and echocardiographic concepts) confirmed sustained diagnostic performance. Within this single-cohort retrospective study, ClinCBM-KD achieves diagnostic performance equivalent to unconstrained post hoc models while enabling transparent, concept-level predictions and counterfactual clinical reasoning; prospective human-centred validation is required to establish whether these capabilities translate to improved clinical practice.