Emergency department (ED) crowding is a global public health issue that has been repeatedly associated with increased mortality. Predicting future service demand would enable preventative measures aiming to eliminate crowding along with it's detrimental effects. Recent findings in our ED indicate that occupancy ratios exceeding 90% are associated with increased 10-day mortality. In this paper, we aim to predict these crisis periods using retrospective data from a large Nordic ED with a LightGBM model. We provide predictions for the whole ED and individually for it's different operational sections. We demonstrate that afternoon crowding can be predicted at 11 a.m. with an AUC of 0.82 (95% CI 0.78-0.86) and at 8 a.m. with an AUC up to 0.79 (95% CI 0.75-0.83). Consequently we show that forecasting mortality-associated crowding using anonymous administrative data is feasible.

翻译：急诊科（ED）拥挤是一个全球性的公共卫生问题，已多次被证实与死亡率上升相关。预测未来的服务需求将有助于采取预防措施，以消除拥挤及其不利影响。我们急诊科的最新研究结果表明，占用率超过90%与10天死亡率增加相关。本文旨在利用来自一家大型北欧急诊科的回顾性数据，通过LightGBM模型预测这些危机时段。我们提供了对整个急诊科及其不同运营分区的单独预测。我们证明，下午的拥挤状况可在上午11点以0.82的AUC值（95% CI 0.78-0.86）进行预测，在上午8点预测的AUC值最高可达0.79（95% CI 0.75-0.83）。因此，我们表明利用匿名管理数据预测与死亡率相关的拥挤是可行的。