Department of Bioelectronics, Modern University of Technology and Information (MTI), Cairo 11571, Egypt.
Department of Electrical and Electronics Engineering, Istanbul Gelisim University, 34310 Avcılar, Turkey.
Sensors (Basel). 2021 Sep 24;21(19):6379. doi: 10.3390/s21196379.
The sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients' priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions' priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley's Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID-19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians' decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies.
COVID-19 重症患者的突然增加迫使医生将患者收入医疗保健机构的重症监护病房（ICU），而这些机构的容量已经超过了需求。为了帮助做出困难的分诊决策，我们提出了一个集成系统 Xtreme Gradient Boosting（XGBoost）分类器和层次分析法（AHP），以帮助卫生当局根据 COVID-19 患者的生物实验室调查结果，确定患者入住 ICU 的优先级。Xtreme Gradient Boosting（XGBoost）分类器用于决定是否应将患者收入 ICU，然后将其应用于 AHP 以对 ICU 的入院优先级进行排名。考虑了 38 个常用的临床变量，并通过 Shapley 的加法解释（SHAP）方法确定了它们的贡献。在这项研究中，比较了五种分类器算法：支持向量机（SVM）、决策树（DT）、K-近邻（KNN）、随机森林（RF）和人工神经网络（ANN），以评估 XGBoost 的性能，而 AHP 系统则将其结果与由经验丰富的临床医生组成的委员会进行了比较。所提出的（XGBoost）分类器具有很高的预测准确性，因为它可以区分需要 ICU 入院的 COVID-19 患者和不需要 ICU 入院的患者，其准确率、灵敏度和特异性分别为 97%、96%和 96%，而 AHP 系统的结果与经验丰富的临床医生确定需要入住 ICU 的患者优先级的决策非常接近。最终，医疗部门可以使用建议的框架对需要入住 ICU 的 COVID-19 患者进行分类，并根据综合 AHP 方法对他们进行优先级排序。
