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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.

Chemotherapy remains a cornerstone of cancer treatment, but it is frequently associated with severe toxicities, with 30–80% of patients experiencing grade 3–4 adverse events that may require dose reduction, treatment delays, or hospitalization. While machine learning models have shown strong potential in predicting chemotherapy-related toxicities using electronic health records, genomic data, and clinical variables, most existing approaches generate only point estimates (e.g., a single risk probability) without quantifying uncertainty, limiting their clinical reliability. Such miscalibrated predictions can lead to overconfident risk underestimation or excessive caution, both of which may negatively impact treatment decisions and patient outcomes. This manuscript proposes a conceptual framework that integrates neural network-based toxicity prediction with adaptive conformal prediction to produce calibrated, patient-specific prediction intervals with formal coverage guarantees. The framework combines a feedforward neural network for risk estimation, a non-conformity score to measure how atypical a patient is relative to the training data, and an adaptive calibration mechanism that updates interval thresholds over time to reflect shifts in patient populations and clinical practice. This design enables narrower intervals for well-represented, predictable cases and wider intervals for atypical or high-uncertainty patients, thereby making prediction reliability explicit. Importantly, the method provides finite-sample coverage guarantees without requiring distributional assumptions, ensuring that true toxicity outcomes fall within the predicted intervals at a user-specified confidence level. By transforming point predictions into uncertainty-aware, clinically interpretable intervals, the framework supports more robust, risk-stratified decision-making in chemotherapy planning and moves toward safer, more trustworthy AI-assisted oncology care.