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Postoperative delirium affects 10–60% of elderly surgical patients and is linked to longer hospital stays, cognitive decline, and increased mortality. Although machine learning models have been developed to predict this condition using perioperative data, most rely on point predictions that fail to express uncertainty, limiting their clinical reliability in high-stakes surgical decision-making. These models often report a single risk estimate without indicating whether predictions are supported by strong or sparse evidence, which can lead to overconfidence and potential patient harm in vulnerable populations with heterogeneous frailty and comorbidity profiles. We argue that Bayesian deep learning is essential for postoperative delirium prediction because it provides distributional outputs and uncertainty estimates that allow clinicians to assess prediction reliability. Incorporating uncertainty quantification can transform these models from opaque tools into clinically trustworthy decision aids. We recommend that uncertainty reporting be required in all predictive models for postoperative delirium and that regulatory and publication standards enforce the use of Bayesian approaches. Overall, replacing point estimates with distributional predictions is necessary to improve safety and clinical utility in perioperative care of elderly patients.

Emergency medical services (EMS) systems face significant challenges in managing fluctuating call volumes, where sudden demand surges can delay response times and worsen outcomes for critical conditions such as cardiac arrest, trauma, and stroke. Accurate forecasting of EMS demand supports proactive ambulance deployment, improving survival rates and resource efficiency, yet traditional methods like ARIMA and exponential smoothing fail to capture nonlinear spatial, temporal, weather, and event-driven effects, and do not provide uncertainty estimates needed for operational decision-making. This paper proposes a conceptual framework based on deep kernel learning with Gaussian processes for spatiotemporal EMS demand forecasting. The model integrates deep neural networks for feature extraction with Gaussian processes for probabilistic inference, enabling both flexible nonlinear representation and uncertainty quantification. It combines temporal, spatial, weather, and event-based kernels to model complex patterns in EMS call volumes. The framework produces predictive mean estimates along with calibrated uncertainty intervals, capturing effects such as weather-driven medical incidents and large public events. This probabilistic output supports risk-aware ambulance allocation strategies that balance over- and under-resourcing. Overall, the proposed approach provides a unified, interpretable, and uncertainty-aware solution for EMS demand forecasting, with future work aimed at validation on real-world datasets and comparison with existing methods.