Saadatmand Sara, Salimifard Khodakaram, Mohammadi Reza, Kuiper Alex, Marzban Maryam, Farhadi Akram
Computational Intelligence and Intelligent Optimization Research Group, Persian Gulf University, Bushehr, 75169 Iran.
Section Business Analytics, Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands.
Ann Oper Res. 2022 Sep 29:1-29. doi: 10.1007/s10479-022-04984-x.
The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburdening of staff and shortages of medical resources. These issues might have affected the quality of healthcare services provided directly impacting a patient's survival. The objective of this research is to leverage Machine Learning (ML) on hospital data in order to support hospital managers and practitioners with the treatment of COVID-19 patients. This is accomplished by providing more detailed inference about a patient's likelihood of ICU admission, mortality and in case of hospitalization the length of stay (LOS). In this pursuit, the outcome variables are in three separate models predicted by five different ML algorithms: eXtreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), Random Forest (RF), bagged-CART (b-CART), and LogitBoost (LB). With the exception of KNN, the studied models show good predictive capabilities when evaluating relevant accuracy scores, such as area under the curve. By implementing an ensemble stacking approach (either a Neural Net or a General Linear Model) on top of the aforementioned ML algorithms the performance is further boosted. Ultimately, for the prediction of admission to the ICU, the ensemble stacking via a Neural Net achieved the best result with an accuracy of over 95%. For mortality at the ICU, the vanilla XGB performed slightly better (1% difference with the meta-model). To predict large length of stays both ensemble stacking approaches yield comparable results. Besides it direct implications for managing COVID-19 patients, the approach presented serves as an example how data can be employed in future pandemics or crises.
近期的新冠疫情影响了全球的医疗系统。特别是，重症监护病房（ICU）在重症患者的治疗中发挥了关键作用。然而，与此同时，由于病毒的广泛传播导致入院人数不断增加，给ICU病房带来了诸多问题，如工作人员负担过重和医疗资源短缺。这些问题可能影响了所提供医疗服务的质量，直接影响患者的生存。本研究的目的是利用机器学习（ML）处理医院数据，以支持医院管理人员和从业者对新冠患者的治疗。这是通过对患者入住ICU的可能性、死亡率以及住院情况下的住院时长（LOS）提供更详细的推断来实现的。在这一过程中，通过五种不同的ML算法在三个单独的模型中预测结果变量：极端梯度提升（XGB）、K近邻（KNN）、随机森林（RF）、袋装CART（b - CART）和逻辑回归提升（LB）。除了KNN之外，在所研究的模型在评估相关准确性分数（如曲线下面积）时显示出良好的预测能力。通过在上述ML算法之上实施集成堆叠方法（神经网络或广义线性模型），性能得到进一步提升。最终，对于ICU入院预测，通过神经网络的集成堆叠取得了最佳结果，准确率超过95%。对于ICU死亡率，普通的XGB表现略好（与元模型相差1%）。对于预测较长的住院时长，两种集成堆叠方法产生了可比的结果。除了对管理新冠患者有直接影响外，所提出的方法还为例示了如何在未来的大流行或危机中利用数据。
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