## 6 Discussion  
This study reveals fundamental tensions and synergies in deploying machine learning for clinical predictive monitoring, where accuracy, calibration, UQ, and explainability  
Figure 26: Comparison of SHAP values for single and both prediction sets under Split conformal prediction with the uncalibrated CatBoost model. (**a**) Single prediction set. (**b**) Both prediction sets. intersect. We selected sepsis management and the prediction of Intensive Care Unit (ICU) admission as our use case for several strategic reasons. Clinically, sepsis is a time-critical, high-mortality condition where early predictive analytics can directly impact patient outcomes. Methodologically, it is an ideal testbed for our framework. The progression of sepsis is inherently a process, lending itself to a Predictive Process Monitoring approach. Furthermore, sepsis data presents a unique combination of challenges that our multilayered framework is designed to address: (1) natural class imbalance, where the critical minority class (ICU admission) must be predicted reliably; (2) high clinical uncertainty, justifying the need for a robust uncertainty quantification (UQ) method like Conformal Prediction; and (3) data heterogeneity, with a complex mix of temporal, numerical, and categorical features that tests the power of modern ensemble models. Thus, sepsis provides a rich, clinically relevant context to evaluate the interplay between prediction, calibration, and explainability. The findings challenge conventional assumptions about post-hoc calibration as a purely technical adjustment, positioning it instead as a transformative layer that reshapes model behavior, trustworthiness, and clinical utility. Below, we synthesize the methodological and practical implications of these insights. Calibration as a Reinterpretive Act.The central paradox uncovered is that calibration methods, while designed to align probabilities with empirical outcomes, inherently re-configure how models \"reason\" about uncertainty. For instance, isotonic regression not only reduced miscalibration but also redirected the model's attention during uncertain predictions toward clinically interpretable markers (e.g., ambiguous lab results or rare diagnoses). In contrast, parametric methods like Platt scaling preserved the model's original feature hierarchy, even when uncertainty arose from non-clinical artifacts like administrative timestamps. This divergence underscores that calibration is not a neutral correction but a reinterpretive process: it filters the model's internal logic through the lens of probability alignment, amplifying or suppressing specific drivers of uncertainty. For clinical applications, this means the choice of calibration directly influences whether uncertainty explanations align with medical intuition or obscure it--a critical factor in fostering clinician trust. The Duality of Uncertainty Quantification.CP provided robust coverage guarantees across all methods, yet its clinical value depended heavily on calibration. Uncalibrated models achieved marginal coverage but exhibited a dangerous skew: 95% of errors impacted the minority class (ICU admissions), reflecting systemic underconfidence in high-risk predictions. Post-hoc calibration rectified this by redistricting uncertainty, tying it to domain-relevant edge cases (e.g., conflicting biomarker trends) rather than arbitrary thresholds. This duality--coverage as a statistical necessity, error distribution as a clinical imperative--highlights that CP frameworks must be calibration-aware to avoid perpetuating hidden biases. In practice, this means CP cannot be treated as a standalone module; its integration with calibration determines whether UQ serves as a safety net or a source of systemic blind spots. Explainability as a Mirror of Calibration Strategy.SHAP analysis exposed how calibration methods rewrite the narrative of model decisions.