Emergency department's (ED) boarding (defined as ED waiting time greater than
four hours) has been linked to poor patient outcomes and health system
performance. Yet, effective forecasting models is rare before COVID-19, lacking
during the peri-COVID era. Here, a hybrid convolutional neural network
(CNN)-Long short-term memory (LSTM) model was applied to public-domain data
sourced from Hong Kong's Hospital Authority, Department of Health, and Housing
Authority. In addition, we sought to identify the phase of the COVID-19
pandemic that most significantly perturbed our complex adaptive healthcare
system, thereby revealing a stable pattern of interconnectedness among its
components, using deep transfer learning methodology.
Our result shows that 1) the greatest proportion of days with ED boarding was
found between waves four and five; 2) the best-performing model for forecasting
ED boarding was observed between waves four and five, which was based on
features representing time-invariant residential buildings' built environment
and sociodemographic profiles and the historical time series of ED boarding and
case counts, compared to during the waves when best-performing forecasting is
based on time-series features alone; and 3) when the model built from the
period between waves four and five was applied to data from other waves via
deep transfer learning, the transferred model enhanced the performance of
indigenous models.

应用了混合卷积神经网络（CNN）- 长短期记忆（LSTM）模型，通过公共领域数据来源于香港的医院管理局、卫生署和房屋管理局，研究急诊科（ED）的等候时间超过四个小时（ED 候诊）与患者结果和卫生系统表现之间的关联。同时，通过深度迁移学习方法，寻找最显著扰乱复杂适应性医疗系统的 COVID-19 流行阶段，揭示其组成部分之间稳定的相互关系模式。本研究的结果表明：1）ED 候诊最多的是第四和第五波之间的天数；2）最佳预测 ED 候诊的模型观察到在第四和第五波之间，该模型基于代表时间不变的住宅建筑环境和社会人口特征，以及 ED 候诊和病例数的历史时间序列，与其他波次的预测最佳模型仅基于时间序列特征相比表现更好；3）当将第四和第五波之间构建的模型应用于其他波次的数据时，通过深度迁移学习，转移模型提高了本土模型的性能。

分析急诊科患者候床现象的变化并在香港 COVID-19 疫情波动中验证预测模型的可转移性：基于混合 CNN-LSTM 方法量化建筑层次的社会生态风险

Analyzing the Variations in Emergency Department Boarding and Testing  the Transferability of Forecasting Models across COVID-19 Pandemic Waves in  Hong Kong: Hybrid CNN-LSTM approach to quantifying building-level  socioecological risk