Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has the potential patient outcomes. Despite active research on the subject, several gaps remain: 1) proposed forecasting models have become outdated due to quick influx of advanced machine learning models (ML), 2) amount of multivariable input data has been limited and 3) discrete performance metrics have been rarely reported. In this study, we document the performance of a set of advanced ML models in forecasting ED occupancy 24 hours ahead. We use electronic health record data from a large, combined ED with an extensive set of explanatory variables, including the availability of beds in catchment area hospitals, traffic data from local observation stations, weather variables, etc. We show that N-BEATS and LightGBM outpeform benchmarks with 11 % and 9 % respective improvements and that DeepAR predicts next day crowding with an AUC of 0.76 (95 % CI 0.69-0.84). To the best of our knowledge, this is the first study to document the superiority of LightGBM and N-BEATS over statistical benchmarks in the context of ED forecasting.

翻译：急诊科拥挤对患者安全构成重大威胁，且已被反复证实与死亡率上升相关。预测未来服务需求有望改善患者预后。尽管该领域已有积极研究，但仍存在以下不足：1）因先进机器学习模型的快速引入，现有预测模型已显陈旧；2）多变量输入数据的量级有限；3）离散性能指标鲜有报道。本研究记录了一组先进机器学习模型在预测未来24小时急诊科占用率方面的性能。我们利用大型联合急诊科的电子健康记录数据，包含广泛的解释变量，如服务区域医院的床位可用性、当地观测站的交通数据、气象变量等。结果表明，N-BEATS和LightGBM分别以11%和9%的改进幅度超越基准模型，且DeepAR预测次日拥挤程度的AUC值为0.76（95%置信区间0.69-0.84）。据我们所知，本研究首次证实了LightGBM与N-BEATS在急诊科预测场景中相较于统计基准模型的优越性。