CARE-Cirrhosis: A multi-level explainability framework integrating predictive modeling and personalized clinical recommendation in cirrhosis care

This study introduces CARE-Cirrhosis (Cirrhosis Ascites Risk Prediction and Explainability with Recommendation Engine), a unified methodological framework that systematically integrates predictive modeling, multi-level explainability, and a personalized recommendation engine into a single, deployable clinical decision-support architecture. Rather than applying interpretability tools in isolation, the framework embeds Explainable AI (XAI) methods like SHapley Additive exPlanations (SHAP), Local Interpretable Model agnostic Explanations (LIME), and counterfactual reasoning within an operational pipeline that transforms predictive outputs into transparent, actionable, patient-specific clinical guidance. Thus, it is advancing the methodological foundations of interpretable machine learning(ML) for biomedical applications. Data from the Mayo Clinic Primary Biliary Cirrhosis (PBC) cohort ( n = 418) were analyzed using Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGB), evaluated under stratified 5-fold cross-validation(CV). Multi-level interpretability was achieved through XAI methods like global attribution (SHAP), local surrogate reasoning (LIME), and counterfactual analysis (DiCE). These layers were synthesized within a unified interpretability framework, linked to a rule-based recommendation engine for generating patient-specific, physiologically plausible “what-if” scenarios. The pipeline was implemented as a mobile application to demonstrate translational applicability and real-time deployment feasibility. All models demonstrated strong discriminative performance (AUROC 0.90–0.92). SHAP identified albumin, platelets, prothrombin, and edema as consistent global predictors, while counterfactual reasoning delineated clinically meaningful feature thresholds (probability 0.3–0.4). The interpretability synthesis enabled cross-validation of feature attributions across explanation paradigms, improving transparency and robustness. The integrated recommendation module generated individualized monitoring strategies and actionable insights. CARE-Cirrhosis establishes a generalizable methodological approach for unifying predictive modeling, explainability, and clinical recommendation within a single, deployable framework. By demonstrating a reproducible process for multi-level interpretability integration, it advances the methodological scope of biomedical informatics beyond model development toward transparent, interpretable, and actionable decision-support systems applicable across clinical domains. • Introduced CARE-Cirrhosis, a unified explainable AI (XAI) framework that integrates predictive modeling, multi-level interpretability XAI methods (SHAP, LIME, DiCE), and personalized recommendation into a single operational architecture. • Embedded interpretability within the model development workflow rather than as a post hoc step, achieving consistent global, local, and counterfactual transparency to enhance clinical trust. • Developed a hybrid recommendation engine that combines rule-based hepatology knowledge with model-driven feature gradients to generate physiologically plausible, patient-specific “what-if” guidance. • Achieved strong predictive performance (AUROC ≈ 0.90–0.92) across LR, RF, and XGB, with robustness confirmed through stratified cross-validation and synthetic external validation on perturbed datasets. • Deployed CARE-Cirrhosis as a mobile application integrating real-time risk scoring, interpretability visualization, and actionable recommendations into a clinician- and patient-friendly interface. • Designed the framework to be disease-agnostic and data schema–independent, enabling adaptation to other biomedical domains such as cardiology, oncology, and chronic disease management.