A meta-learning ensemble framework for robust and interpretable prediction of emergency medical services demand

Comprehensive Summary

This study explores the use of advanced machine learning methodologies to enhance the accuracy, robustness, and generalizability of Emergency Medical Services (EMS) demand forecasting. Traditional forecasting approaches often rely on single models and are limited in their ability to adapt to regional variability, temporal fluctuations, and data scarcity, particularly in low-resource settings. To address these limitations, the authors introduce a meta-learning ensemble framework that combines multiple predictive models using a linear regression meta-learner. The system integrates historical EMS call data with temporal and meteorological variables, enabling the model to learn higher-level patterns across different environments and improve adaptability to local conditions. This design allows the forecasting framework to remain effective even when individual datasets are limited or heterogeneous.

Outcomes and Implications

The findings show that the meta-learning ensemble consistently outperforms standalone baseline models in both predictive accuracy and stability across multiple settings. Performance gains are especially pronounced in regions with sparse data, demonstrating the model's robustness and transferability. Importantly, the use of SHAP (Shapley Additive Explanations) enhances interpretability by identifying the relative contributions of key predictors, such as weather patterns and time-dependent trends, to EMS demand. This transparency supports informed decision-making by EMS administrators and policymakers, facilitating more efficient resource allocation and operational planning. The study demonstrates that interpretable, ensemble-based machine learning systems can strengthen public health infrastructure, optimize prehospital care delivery, and support data-driven decision-making in emergency medical services.