用机器学习预测急诊科拥挤高峰期，提前预警死亡风险。
Forecasting mortality associated emergency department crowding
- 基于历史数据用LightGBM模型预测急诊拥挤
- 上午11点预测下午拥挤，准确率AUC达0.82
- 可为医院提前调配资源，适合医疗管理者
急诊科拥挤是全球性的公共卫生问题，与死亡率升高密切相关。预测未来服务需求可推动预防性措施，以消除拥挤及其负面影响。我们所在急诊科的近期研究发现，占用率超过90%时，10天内死亡率上升。本文利用来自北欧大型急诊科的回顾性数据，采用LightGBM模型预测危机时段，分别对整个急诊科及各功能区进行预测。结果表明，可在上午11点预测下午的拥挤情况，AUC达到0.82（95%置信区间0.78–0.86）；在上午8点预测的AUC最高达0.79（95%置信区间0.75–0.83）。因此，本研究证明仅使用匿名行政数据即可实现与死亡相关的急诊拥挤预测。
原文摘要 · Abstract (English)
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.
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