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Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19.

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Title: Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19.
Authors: Guo, Guangyu1 (AUTHOR) gyguo95@gmail.com, Liu, Zhuoyan1 (AUTHOR) lzy8962@gmail.com, Zhao, Shijie1 (AUTHOR) shijiezhao666@gmail.com, Guo, Lei1 (AUTHOR) lguo@nwpu.edu.cn, Liu, Tianming2 (AUTHOR) tianming.liu@gmail.com
Source: IEEE Journal of Biomedical & Health Informatics. May2021, Vol. 25 Issue 5, p1347-1357. 11p.
Subjects: Microsoft Internet explorer (Computer software), COVID-19, COVID-19 pandemic, Reverse transcriptase polymerase chain reaction, Computed tomography, Random forest algorithms, COVID-19 testing
Abstract: The coronavirus disease 2019 (COVID-19) has swept all over the world. Due to the limited detection facilities, especially in developing countries, a large number of suspected cases can only receive common clinical diagnosis rather than more effective detections like Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests or CT scans. This motivates us to develop a quick screening method via common clinical diagnosis results. However, the diagnostic items of different patients may vary greatly, and there is a huge variation in the dimension of the diagnosis data among different suspected patients, it is hard to process these indefinite dimension data via classical classification algorithms. To resolve this problem, we propose an Indefiniteness Elimination Network (IE-Net) to eliminate the influence of the varied dimensions and make predictions about the COVID-19 cases. The IE-Net is in an encoder-decoder framework fashion, and an indefiniteness elimination operation is proposed to transfer the indefinite dimension feature into a fixed dimension feature. Comprehensive experiments were conducted on the public available COVID-19 Clinical Spectrum dataset. Experimental results show that the proposed indefiniteness elimination operation greatly improves the classification performance, the IE-Net achieves 94.80% accuracy, 92.79% recall, 92.97% precision and 94.93% AUC for distinguishing COVID-19 cases from non-COVID-19 cases with only common clinical diagnose data. We further compared our methods with 3 classical classification algorithms: random forest, gradient boosting and multi-layer perceptron (MLP). To explore each clinical test item's specificity, we further analyzed the possible relationship between each clinical test item and COVID-19. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Journal of Biomedical & Health Informatics is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19.
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  Data: <searchLink fieldCode="AR" term="%22Guo%2C+Guangyu%22">Guo, Guangyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gyguo95@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zhuoyan%22">Liu, Zhuoyan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lzy8962@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Shijie%22">Zhao, Shijie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shijiezhao666@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Guo%2C+Lei%22">Guo, Lei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lguo@nwpu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Tianming%22">Liu, Tianming</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> tianming.liu@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Journal+of+Biomedical+%26+Health+Informatics%22">IEEE Journal of Biomedical & Health Informatics</searchLink>. May2021, Vol. 25 Issue 5, p1347-1357. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Microsoft+Internet+explorer+%28Computer+software%29%22">Microsoft Internet explorer (Computer software)</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19+pandemic%22">COVID-19 pandemic</searchLink><br /><searchLink fieldCode="DE" term="%22Reverse+transcriptase+polymerase+chain+reaction%22">Reverse transcriptase polymerase chain reaction</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19+testing%22">COVID-19 testing</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: The coronavirus disease 2019 (COVID-19) has swept all over the world. Due to the limited detection facilities, especially in developing countries, a large number of suspected cases can only receive common clinical diagnosis rather than more effective detections like Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests or CT scans. This motivates us to develop a quick screening method via common clinical diagnosis results. However, the diagnostic items of different patients may vary greatly, and there is a huge variation in the dimension of the diagnosis data among different suspected patients, it is hard to process these indefinite dimension data via classical classification algorithms. To resolve this problem, we propose an Indefiniteness Elimination Network (IE-Net) to eliminate the influence of the varied dimensions and make predictions about the COVID-19 cases. The IE-Net is in an encoder-decoder framework fashion, and an indefiniteness elimination operation is proposed to transfer the indefinite dimension feature into a fixed dimension feature. Comprehensive experiments were conducted on the public available COVID-19 Clinical Spectrum dataset. Experimental results show that the proposed indefiniteness elimination operation greatly improves the classification performance, the IE-Net achieves 94.80% accuracy, 92.79% recall, 92.97% precision and 94.93% AUC for distinguishing COVID-19 cases from non-COVID-19 cases with only common clinical diagnose data. We further compared our methods with 3 classical classification algorithms: random forest, gradient boosting and multi-layer perceptron (MLP). To explore each clinical test item's specificity, we further analyzed the possible relationship between each clinical test item and COVID-19. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Journal of Biomedical & Health Informatics is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1109/JBHI.2021.3060035
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 11
        StartPage: 1347
    Subjects:
      – SubjectFull: Microsoft Internet explorer (Computer software)
        Type: general
      – SubjectFull: COVID-19
        Type: general
      – SubjectFull: COVID-19 pandemic
        Type: general
      – SubjectFull: Reverse transcriptase polymerase chain reaction
        Type: general
      – SubjectFull: Computed tomography
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: COVID-19 testing
        Type: general
    Titles:
      – TitleFull: Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19.
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            NameFull: Guo, Guangyu
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            NameFull: Liu, Zhuoyan
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            NameFull: Zhao, Shijie
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            NameFull: Guo, Lei
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            NameFull: Liu, Tianming
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            – D: 01
              M: 05
              Text: May2021
              Type: published
              Y: 2021
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              Value: 25
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              Value: 5
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            – TitleFull: IEEE Journal of Biomedical & Health Informatics
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