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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1016/j.eswa.2022.116547
|2 doi
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|a eng
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| 100 |
1 |
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|a Shen, Jiang
|e verfasserin
|4 aut
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| 245 |
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|a Decision support analysis for risk identification and control of patients affected by COVID-19 based on Bayesian Networks
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 09.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2022 Elsevier Ltd. All rights reserved.
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|a 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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| 650 |
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|a Journal Article
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|a Bayesian networks (BNs)
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| 650 |
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|a COVID-19
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| 650 |
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|a Decision support analysis
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| 650 |
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|a Machine learning
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| 650 |
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4 |
|a Risk identification and control
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| 700 |
1 |
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|a Liu, Fusheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Man
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fu, Lipeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Dong, Zhenhe
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Jiachao
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Expert systems with applications
|d 1999
|g 196(2022) vom: 15. Juni, Seite 116547
|w (DE-627)NLM098196782
|x 0957-4174
|7 nnns
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| 773 |
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|g volume:196
|g year:2022
|g day:15
|g month:06
|g pages:116547
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|u http://dx.doi.org/10.1016/j.eswa.2022.116547
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