如果你关注医疗预测或时间序列，这篇论文的HierSTT模型用一个框架搞定多层级预测，WAPE降了32%，还开源了数据集和代码。
急诊部门（ED）面临患者需求不确定、季节性激增等压力，现有方法常独立预测单一层级，导致医院、区域、国家预测不连贯。研究者提出HierSTT，一个基于Transformer的层次化框架，在单一端到端模型中联合预测医院、区域和国家三级需求。该模型使用时序融合Transformer捕捉国家级动态，并通过时空Transformer编码器-解码器模块基于高层预测建模区域和医院需求。在覆盖葡萄牙81家医院、5个区域卫生管理部门的数据集上，HierSTT相比最佳非层次化深度学习基线将平均WAPE降低32%，并优于所有经典层次协调方法。
Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting
Emergency Departments (EDs) are critical access points in healthcare systems, yet they face persistent pressure from unpredictable patient demand, seasonal surges, and non-urgent visits. Effective ED planning requires forecasts at multiple decision-making levels: hospitals need local demand estimates for staffing and bed management, regions require forecasts to coordinate healthcare units, and national authorities need system-wide projections for capacity planning. However, most existing approaches forecast ED demand independently at a single level, ignoring the hierarchy linking hospitals, regions, and national systems. This can produce incoherent predictions, where hospital-level forecasts do not aggregate consistently to regional or national demand. We propose HierSTT, a hierarchical Transformer-based framework for coherent multi-level ED forecasting. HierSTT jointly predicts hospital, regional, and national level demand in a single end-to-end model. A Temporal Fusion Transformer captures national dynamics, while spatio-temporal Transformer encoder-decoder modules model regional and hospital demand conditioned on higher-level forecasts. A coherence-aware loss penalizes cross-level inconsistencies during training. We further introduce a nationwide Portuguese ED dataset covering 81 hospitals across 5 regional health administrations, with heterogeneous covariates at each level. Experiments show that HierSTT reduces average WAPE by 32\% relative to the best non-hierarchical deep learning baseline and outperforms all classical hierarchical reconciliation methods, while producing near-coherent predictions across levels. Additional resources associated with this work are available at https://github.com/FilipaLino/HierSTT.