用于连贯急诊科预测的分层时空Transformer
Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting
- Institute for Systems and Robotics（系统与机器人研究所）
- LARSyS
- Instituto Superior Técnico（高等技术学院）
- NOVA School of Science and Technology（NOVA科学技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对现有急诊科预测方法忽略层级关联导致预测不连贯的问题，提出HierSTT分层时空Transformer框架，在葡萄牙ED数据集上实现多层面连贯预测，性能优于非分层基线及经典分层协调方法。
AI中文摘要：
急诊科（ED）是医疗系统的关键接入点，但面临着来自不可预测的患者需求、季节性激增和非紧急就诊的持续压力。有效的急诊科规划需要在多个决策层面进行预测：医院需要本地需求估计以进行人员配置和床位管理，地区需要预测以协调医疗单位，国家当局需要全系统预测以进行容量规划。然而，大多数现有方法在单一层面独立预测急诊科需求，忽略了连接医院、地区和国家系统的层级关系，这可能产生不连贯的预测，即医院层面的预测无法一致地聚合为地区或国家需求。我们提出HierSTT，一种基于Transformer的分层框架，用于连贯的多层面急诊科预测。HierSTT在单个端到端模型中联合预测医院、地区和国家层面的需求，其中时间融合Transformer捕捉国家动态，而时空Transformer编码器-解码器模块以更高层面的预测为条件对地区和医院需求进行建模。一种感知连贯性的损失函数在训练过程中惩罚跨层面的不一致性。我们还引入了一个全国性的葡萄牙急诊科数据集，涵盖5个地区卫生管理局的81家医院，每个层面都有异质协变量。实验表明，与最佳非分层深度学习基线相比，HierSTT将平均WAPE降低了32%，并且优于所有经典的分层协调方法，同时在各层面产生接近连贯的预测。本工作的额外资源可在此https URL获取。
英文摘要：
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.
