Decision support analysis for risk identification and control of patients affected by COVID-19 based on Bayesian Networks.
Decision support analysis for risk identification and control of patients affected by COVID-19 based on Bayesian Networks.
复制标题
基于贝叶斯网络影响的患者的风险识别和控制患者的风险识别和控制的决策支持分析。
DOI：
10.1016/j.eswa.2022.116547
复制
发表时间：
2022-06-15
影响因子：
8.5
通讯作者：
Wu J
中科院分区：
文献类型：
--
作者：
Shen J;Liu F;Xu M;Fu L;Dong Z;Wu J
在冠状病毒病（COVID-19）爆发的背景下，本文提出了一种基于贝叶斯网络（BNs）的创新且系统的决策支持模型，以识别和控制COVID-19患者传播病毒的风险，需要以下三个步骤。首先，通过查阅相关文献并结合专家知识，我们对COVID-19的特征（风险因素）进行识别和分类，获得了COVID-19风险评估贝叶斯网络（CRABN）的概念框架。其次，从中国湖北省的医院收集了具有患者风险级别专家评分结果的COVID-19患者数据作为训练集，通过机器学习获得了CRABNs模型的结构和参数。最后，我们提出两个指标，即模型偏差和模型准确性，并利用剩余数据验证CRABNs模型的可行性和有效性，以确保模型的预测结果与具有治疗COVID-19相关经验的专家提供的实际结果不存在显着差异。同时，我们通过准确率、灵敏度、特异性和 F-score 四个指标将 CRABNs 模型与支持向量机（SVM）、随机森林（RF）和 k 最近邻（KNN）模型进行比较。结果表明该模型的可靠性并表明其具有良好的应用潜力。所提出的模型可以在全球范围内被医院医生用作决策支持工具，以提高评估患者 COVID-19 症状严重程度的准确性。此外，未来随着模型的进一步完善，可用于流行病领域的风险评估。
In the context of the outbreak of coronavirus disease (COVID-19), this paper proposes an innovative and systematic decision support model based on Bayesian networks (BNs) to identify and control the risk of COVID-19 patients spreading the virus, which requires the following three steps. First, by consulting the related literature and combining this with expert knowledge, we identify and classify the characteristics (risk factors) of COVID-19 and obtain a conceptual framework for COVID-19 Risk Assessment Bayesian Networks (CRABNs). Second, data on COVID-19 patients with expert scoring results on patient risk levels were collected from hospitals in Hubei Province of China and are used as the training set, and the structure and parameters of the CRABNs model are obtained through machine learning. Finally, we propose two indicators, namely, Model Bias and Model Accuracy, and use the remaining data to verify the feasibility and effectiveness of the CRABNs model to ensure that there are no significant differences between the predicted results of the model and the actual results provided by experts who have relevant experience in treating COVID-19. At the same time, we compared the CRABNs model with the support vector machine (SVM), random forest (RF), and k-nearest neighbour (KNN) models through four indicators: accuracy, sensitivity, specificity, and F-score. The results suggest the reliability of the model and show that it has promising application potential. The proposed model can be used globally by doctors in hospitals as a decision support tool to improve the accuracy of assessing the severity of COVID-19 symptoms in patients. Furthermore, with the further improvement of the model in the future, it can be used for risk assessments in the field of epidemics.
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