论文标题
预测急诊室容量限制了共同隔离床
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds
论文作者
论文摘要
预测医院环境中的患者量是时间序列预测的良好应用。现有工具通常在每日或每周级别进行预测，以帮助计划人员配备。在我们儿科医院的急诊室放置了与Covid相关的新容量限制的原因，我们开发了一个小时的预测工具，可以在24小时内进行预测。这些预测将使我们的医院有足够的时间能够武装资源来扩大能力和增加员工（例如，转变病房或引入医生）。使用高斯工艺回归（GPRS），我们在预测医院容量的序数层时（平均精度/召回率：82％/74％）时，我们都能为两个点预测（平均R平均：82％）以及分类精度获得强劲的性能。与传统的回归方法相比，GPRS不仅获得了持续更高的性能，而且对整个2020年发生的数据集偏移也具有强大的稳健性。由于我们的结果的实力，鼓励医院利益相关者，我们目前正在努力将工具转移到实时环境中，以增强我们的医护人员的能力。
Predicting patient volumes in a hospital setting is a well-studied application of time series forecasting. Existing tools usually make forecasts at the daily or weekly level to assist in planning for staffing requirements. Prompted by new COVID-related capacity constraints placed on our pediatric hospital's emergency department, we developed an hourly forecasting tool to make predictions over a 24 hour window. These forecasts would give our hospital sufficient time to be able to martial resources towards expanding capacity and augmenting staff (e.g. transforming wards or bringing in physicians on call). Using Gaussian Process Regressions (GPRs), we obtain strong performance for both point predictions (average R-squared: 82%) as well as classification accuracy when predicting the ordinal tiers of our hospital's capacity (average precision/recall: 82%/74%). Compared to traditional regression approaches, GPRs not only obtain consistently higher performance, but are also robust to the dataset shifts that have occurred throughout 2020. Hospital stakeholders are encouraged by the strength of our results, and we are currently working on moving our tool to a real-time setting with the goal of augmenting the capabilities of our healthcare workers.
