## 5 Results
### SHAP-Based Explainability Analysis  
The integration of calibration methods with CP not only refines probabilistic outputs but also reshapes the interpretability of predictive models, particularly in distinguishing high-confidence predictions from uncertain ones. To address RQ5, SHAP analysis was employed to dissect feature attributions across calibration strategies, revealing how post-hoc adjustments influence the drivers of certainty and uncertainty in ICU admission predictions. By comparing SHAP values for instances classified as certain (single-label sets) versus uncertain (both-label sets) under SCP, this analysis elucidates the interplay between calibration techniques and model interpretability in clinically actionable terms (see Figures 21-26). _Feature Attribution Patterns Across Calibration Methods._ For certain predictions, administrative and temporal features--such as age, duration_since_registration, and hours_past_midnight--consistently emerged as dominant contributors across all calibration methods. These features, which encode patient demographics and care timeline metadata, serve as robust anchors for high-confidence predictions, reflecting their stability in capturing systemic risk factors for sepsis progression. Clinical markers like diagnose_C (a diagnostic code for sepsis) and diagnosticurinaryculture (urinary tract infection indicators) also retained prominence, underscore their established relevance in sepsis care pathways. This consistency suggests that calibration methods preserve the model's reliance on well-understood predictors when confidence is high, aligning with clinical intuition. In contrast, uncertain predictions exhibited marked divergence in feature importance depending on the calibration approach. Under Beta calibration, uncertainty was primarily linked to rare diagnostic codes (e.g., diagnose_BE) and biochemical markers like diagnosticatastrup (arterial blood gas analysis), which are less frequently observed in sepsis cases. This pattern implies that ambiguity arises when the model encounters atypical clinical profiles, where sparse or conflicting laboratory results complicate risk assessment. Isotonic Regression, however, tied uncertainty to deviations from standard diagnostic pathways, emphasizing interactions between temporal features (duration_last_event) and less common lab tests (diagnosticurinarysediment). Such shifts highlight how non-parametric calibration amplifies the salience of edge-case clinical signals, potentially flagging patients whose trajectories defy conventional sepsis criteria. probability outputs, may inadvertently magnify the impact of transient or noisy clinical measurements. Venn-Abers calibration, despite its theoretical guarantees for validity, produced less coherent explanations for uncertain predictions, with paradoxical retention of administrative features (duration_since_reg) as key drivers even amid ambiguity. This discordance between feature importance and prediction uncertainty could undermine clinician trust, as administrative timestamps lack direct pathophysiological relevance to sepsis severity. Parametric methods like Platt Scaling exhibited a hybrid behavior: certain predictions mirrored uncalibrated models in prioritizing age and diagnose_C, but uncertain predictions disproportionately emphasized temporal features (hours_past_midnight), divorcing explanations from clinical context. This misalignment indicates that logistic adjustments, while effective in probability correction, may obscure the biological rationale for uncertainty, rendering explanations less actionable. The uncalibrated model, unsurprisingly, displayed erratic attributions for uncertain cases, with rare diagnostic codes (diagnose_AB) and administrative artifacts dominating SHAP values--a consequence of unregulated probability overconfidence amplifying noise in feature space. Figure 21: Comparison of SHAP values for single and both prediction sets under Split conformal prediction with post-hoc Beta calibration applied to the CatBoost model. (**a**) Single prediction set.