## 6 Discussion
While all methods agreed on feature importance for high-confidence predictions (e.g., prioritizing age or care timeline metadata), their handling of uncertainty diverged starkly. Non-parametric methods like Isotonic Regression and Spline calibration linked ambiguity to clinically meaningful signals--aberrant lab results, atypical diagnostic codes--effectively translating uncertainty into diagnostic hypotheses. Parametric methods, however, often attributed uncertainty to non-clinical features (e.g., hours_past_midnight), creating explanations that clash with clinician reasoning. This suggests that explainability is not static but calibration-contingent: the same model can oscillate between clinically coherent and opaque explanations based on post-processing choices. For healthcare AI, this demands a paradigm shift--evaluating models not just by their accuracy or calibration metrics, but by how their entire decision hierarchy (certain and uncertain) aligns with domain expertise. Methodological Trade-offs and Clinical RealitiesThe trade-offs between calibration approaches carry profound implications for real-world deployment. Isotonic Regression, despite its superior alignment of uncertainty with clinical logic, introduces computational complexity and sensitivity to small sample sizes. Platt Scaling, while efficient and stable, risks \"explaining away\" uncertainty through non-clinical features, potentially eroding trust. These tensions necessitate context-aware calibration strategies: in resource-constrained settings, Platt Scaling's efficiency might outweigh its explanatory limitations, while in tertiary care systems, Isotonic Regression's clinical coherence could justify its overhead. Importantly, no method universally dominated, underscoring the need for calibration to be tailored to institutional priorities--whether interpretability, computational efficiency, or strict probability alignment. Our study focused on the deep analysis of a single, state-of-the-art sequential ensemble model (gradient boosting) as the engine for our predictive framework. We acknowledge that this is one of several powerful paradigms in ensemble learning. An alternative approach involves the fusion of multiple, heterogeneous base classifiers. Recent advancements in this area offer promising avenues for further improvement. For instance, theoretical frameworks for the optimal linear soft fusion of classifiers have been developed to combine model outputs in a way that provably minimizes post-fusion error [22]. Similarly, information-theoretic methods such as alpha-integration provide a principled and generalized approach for fusing the probability distributions from diverse models. Future work could explore whether a \"super ensemble,\" created by fusing the outputs of a gradient boosting model with other classifiers (e.g., a deep neural network) using these optimal techniques, could provide an even more robust and reliable starting point for our subsequent calibration and conformal prediction pipeline. This could potentially lead to further reductions in predictive error and more precise uncertainty estimates. Toward a Meta-Analysis of Model DesignThese findings collectively argue for a holistic, systems-level view of predictive model design. Accuracy, calibration, uncertainty, and explainability are not isolated components but interacting dimensions that co-determine clinical utility. For example, a model with stellar AUROC but poorly calibrated probabilities may harm care pathways by overtriggering interventions, while a well-calibrated model with opaque explanations may stagnate due to clinician skepticism. This interdependence suggests that future frameworks must adopt multivariate evaluation metrics that weigh these dimensions jointly, rather than optimizing them in isolation. Beyond statistical performance, the practical deployment of this framework in a clinical setting requires addressing challenges such as latency, interpretability, and trust. Our approach was designed with these factors in mind.