Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Center for Optimization and Intelligent Decision Making in Healthcare Systems (COID-Health), Isfahan University of Technology, Isfahan, 84156-83111, Iran.
BMC Med Inform Decis Mak. 2022 May 5;22(1):123. doi: 10.1186/s12911-022-01861-2.
Coronavirus outbreak (SARS-CoV-2) has become a serious threat to human society all around the world. Due to the rapid rate of disease outbreaks and the severe shortages of medical resources, predicting COVID-19 disease severity continues to be a challenge for healthcare systems. Accurate prediction of severe patients plays a vital role in determining treatment priorities, effective management of medical facilities, and reducing the number of deaths. Various methods have been used in the literature to predict the severity prognosis of COVID-19 patients. Despite the different appearance of the methods, they all aim to achieve generalizable results by increasing the accuracy and reducing the errors of predictions. In other words, accuracy is considered the only effective factor in the generalizability of models. In addition to accuracy, reliability and consistency of results are other critical factors that must be considered to yield generalizable medical predictions. Since the role of reliability in medical decisions is significant, upgrading reliable medical data-driven models requires more attention.
This paper presents a new modeling technique to specify and maximize the reliability of results in predicting the severity prognosis of COVID-19 patients. We use the well-known classic regression as the basic model to implement our proposed procedure on it. To assess the performance of the proposed model, it has been applied to predict the severity prognosis of COVID-19 by using a dataset including clinical information of 46 COVID-19 patients. The dataset consists of two types of patients' outcomes including mild (discharge) and severe (ICU or death). To measure the efficiency of the proposed model, we compare the accuracy of the proposed model to the classic regression model.
The proposed reliability-based regression model, by achieving 98.6% sensitivity, 88.2% specificity, and 93.10% accuracy, has better performance than classic accuracy-based regression model with 95.7% sensitivity, 85.5% specificity, and 90.3% accuracy. Also, graphical analysis of ROC curve showed AUC 0.93 (95% CI 0.88-0.98) and AUC 0.90 (95% CI 0.85-0.96) for classic regression models, respectively.
Maximizing reliability in the medical forecasting models can lead to more generalizable and accurate results. The competitive results indicate that the proposed reliability-based regression model has higher performance in predicting the deterioration of COVID-19 patients compared to the classic accuracy-based regression model. The proposed framework can be used as a suitable alternative for the traditional regression method to improve the decision-making and triage processes of COVID-19 patients.
冠状病毒爆发（SARS-CoV-2）已成为全世界人类社会的严重威胁。由于疾病爆发的速度快，医疗资源严重短缺，因此对医疗系统来说，预测 COVID-19 疾病的严重程度仍然是一个挑战。准确预测重症患者对于确定治疗优先级、有效管理医疗设施和减少死亡人数至关重要。文献中已经使用了各种方法来预测 COVID-19 患者的严重程度预后。尽管这些方法的外观不同，但它们都是通过提高准确性和减少预测误差来实现可推广的结果。换句话说，准确性被认为是模型可推广性的唯一有效因素。除了准确性之外，结果的可靠性和一致性也是产生可推广的医学预测的其他关键因素。由于可靠性在医疗决策中的作用非常重要，因此需要更多关注升级可靠的基于医学数据的模型。
本文提出了一种新的建模技术，用于指定和最大化结果的可靠性，以预测 COVID-19 患者的严重程度预后。我们使用著名的经典回归作为基本模型，并在其上实施我们提出的程序。为了评估所提出模型的性能，我们将其应用于使用包括 46 名 COVID-19 患者临床信息的数据集来预测 COVID-19 的严重程度预后。该数据集包括两种类型的患者结局，包括轻症（出院）和重症（ICU 或死亡）。为了衡量所提出模型的效率，我们将所提出模型的准确性与经典回归模型进行了比较。
所提出的基于可靠性的回归模型的灵敏度为 98.6%、特异性为 88.2%、准确性为 93.10%，优于经典基于准确性的回归模型的灵敏度为 95.7%、特异性为 85.5%、准确性为 90.3%。此外，ROC 曲线的图形分析分别显示经典回归模型的 AUC 为 0.93（95%CI 0.88-0.98）和 AUC 为 0.90（95%CI 0.85-0.96）。
在医疗预测模型中最大化可靠性可以产生更可推广和准确的结果。竞争结果表明，与经典基于准确性的回归模型相比，所提出的基于可靠性的回归模型在预测 COVID-19 患者病情恶化方面具有更高的性能。所提出的框架可以作为传统回归方法的合适替代方法，用于改善 COVID-19 患者的决策和分诊过程。
