预测急诊科留观时间，提前预警拥堵，助力医院调度决策。
An Integrated Forecasting Prototype for Emergency Department Boarding Time to Support Proactive Operational Decision Making
- 用分解与归一化深度模型预测6至24小时急诊留观时间。
- 在真实数据上多时域预测准确率高，极端拥堵下仍稳定有效。
- 开发可落地的MLOps应用，支持数据接入、可视化与模型迭代。
急诊科拥挤是全球性的运营难题，导致诊疗延迟和后续拥堵。急诊留观时间（即住院床位未就位时患者滞留急诊科的时间）是反映该问题的关键指标。提前预测留观时间可支持拥堵发生前的主动决策。本文构建并评估了一种多时域时间序列预测框架，用于预测6、8、10、12及24小时后的急诊留观时间。基于美国一所城市高校附属医院的真实数据，并融合天气、节假日和重大本地事件等外部上下文信息。基于分解的线性模型（DLinear）和基于归一化的线性模型（NLinear）在多个预测时域上表现优异。模型还在高拥堵场景（留观时间显著升高）下进行了评估。此外，开发了一个机器学习运维（MLOps）Web原型应用，支持数据集成、预测可视化、实验与模型重训练，推动该框架的实际落地。
原文摘要 · Abstract (English)
Overcrowding in emergency departments (ED) remains a persistent operational challenge worldwide, causing delays in care delivery and downstream congestion. ED boarding time, defined as the duration admitted patients remain in the ED while awaiting inpatient bed placement, is a key indicator of this congestion. Predicting ED boarding time in advance enables proactive operational decision making before congestion escalates. We developed and evaluated a multi-horizon time series forecasting framework to predict ED boarding time at 6, 8, 10, 12, and 24-hour horizons. Real-world data from a university-affiliated urban hospital in the United States were utilized and integrated with external contextual data sources, including weather, holidays, and major local events. Decomposition-based Linear (DLinear) and Normalization-based Linear (NLinear) time series forecasting deep learning models showed superior performance across multiple horizons. Models were also evaluated under extreme congestion scenarios characterized by elevated boarding times. In addition, a Machine Learning Operations (MLOps) web application prototype was developed to support translation of the forecasting framework into practice through integrated data ingestion, forecast visualization, experimentation, and retraining.
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