MWED-Former: A Multi-Scale Weather-Aware Transformer for Emergency Department Demand Forecasting with Weather Bias Correction
DOI:
https://doi.org/10.66238/fsrma100Keywords:
Emergency Department Forecasting, Multi-Scale Weather Correction, Transformer, Numerical Weather Prediction, Deep Learning, Healthcare Intelligence, Time Series ForecastingAbstract
Emergency Department (ED) overcrowding has become a critical challenge for modern healthcare systems, leading to increased patient waiting times, inefficient resource allocation, and reduced service quality. Accurate forecasting of ED demand is therefore essential for intelligent hospital management and emergency resource optimization. However, existing forecasting approaches primarily rely on historical patient arrival records and often neglect the influence of weather conditions or directly utilize Numerical Weather Prediction (NWP) outputs without correcting forecast biases, limiting their effectiveness under complex meteorological environments. To address these issues, this study proposes MWED-Former (Multi-scale Weather-aware Emergency Demand Transformer), a novel deep learning framework that integrates multi-scale weather bias correction with Transformer-based temporal forecasting. Specifically, a Multi-scale Weather Correction Encoder is designed to capture weather patterns at local, city, and regional scales, while a Weather-aware Fusion Encoder employs cross-attention to model interactions between corrected weather features and historical ED demand. Furthermore, a Dual-Temporal Transformer is introduced to jointly learn short-term fluctuations and long-term periodic trends in patient arrivals. Experimental results on real-world hospital and meteorological datasets demonstrate that MWED-Former achieves an MAE of 7.94, RMSE of 11.87, and MAPE of 7.25, outperforming state-of-the-art baselines such as Informer, Autoformer, and PatchTST. Under extreme weather conditions, the model maintains strong robustness with an MAE of 9.18 and improved classification performance. These results confirm that integrating multi-scale weather bias correction with Transformer-based temporal modeling significantly enhances ED demand forecasting accuracy and reliability for healthcare resource planning.
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