用深度学习提前6小时预测急诊科候诊人数，缓解拥挤问题。
Deep Learning-Based Forecasting of Boarding Patient Counts to Address ED Overcrowding
- 基于运营与环境数据，构建无患者隐私的预测框架。
- 最佳模型误差仅4.30，准确捕捉极端高峰时段趋势。
- 适合医院管理与智慧医疗研究者参考。
本研究提出一种基于深度学习的框架，仅使用操作与环境数据（如急诊科追踪系统、住院人数、天气、节假日和本地活动），不依赖患者级信息，提前六小时预测急诊科候诊人数。数据按小时聚合并经全面特征工程处理，平均候诊人数为28.7（标准差=11.2）。采用ResNetPlus、TSTPlus和TSiTPlus等多个深度学习模型，并通过Optuna优化，其中TSTPlus表现最优（平均绝对误差=4.30，均方误差=29.47，决定系数R²=0.79）。该框架在极端时期仍具高预测精度，表明更广泛的输入特征能显著提升准确性，支持医院主动管理决策，为缓解急诊科拥堵提供实用方案。
原文摘要 · Abstract (English)
This study presents a deep learning-based framework for predicting emergency department (ED) boarding counts six hours in advance using only operational and contextual data, without patient-level information. Data from ED tracking systems, inpatient census, weather, holidays, and local events were aggregated hourly and processed with comprehensive feature engineering. The mean ED boarding count was 28.7 (standard deviation = 11.2). Multiple deep learning models, including ResNetPlus, TSTPlus, and TSiTPlus, were trained and optimized using Optuna, with TSTPlus achieving the best results (mean absolute error = 4.30, mean squared error = 29.47, R2 = 0.79). The framework accurately forecasted boarding counts, including during extreme periods, and demonstrated that broader input features improve predictive accuracy. This approach supports proactive hospital management and offers a practical method for mitigating ED overcrowding.
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