Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests

  1. Haochen Yao
  2. Nan Zhang
  3. Ruochi Zhang
  4. Meiyu Duan
  5. Tianqi Xie
  6. Jiahui Pan
  7. Ejun Peng
  8. Juanjuan Huang
  9. Yingli Zhang
  10. Xiaoming Xu
  11. Hong Xu
  12. Fengfeng Zhou
  13. Guoqing Wang

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  1. Version published to 10.3389/fcell.2020.00683
  2. ScreenIT

    SciScore for 10.1101/2020.07.27.20044990: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The above algorithms are implemented using Python programming language (version 3.6) and Scikit-learn package (version 0.22).
    Python
    suggested: (IPython, RRID:SCR_001658)
    Scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    The metric aAcc was used to find the best prediction model.
    aAcc
    suggested: (AACC, RRID:SCR_011999)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are some limitations that should be noted. First, the number of patients with COVID-19 is relatively small, which may limit the accuracy of severeness detection model. Second, since all subjects in our study were Chinese patients with COVID-19, the results may not be applied to other ethnicities. Third, the data of this study is only the preliminary establishment of COVID-19 severeness detection model. Further studies are still needed. This study utilized the machine learning algorithms to detect the COVID-19 severely ill patients from those with only mild symptoms. Our experimental data demonstrated strong correlations with the COVID-19 severeness. And the final COVID-19 severeness detection model achieved the accuracy 0.8148 on the independent test dataset using only 28 clinical biomarkers. The detection model itself is in urgent need for the current epidemic situation that the severely ill patients are at a very high mortality rate. The 28 biomarkers may also be investigated for their underlining mechanisms of their roles in the COVID-19 severely ill patients.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. ScreenIT

    SciScore for 10.1101/2020.07.27.20044990: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The above algorithms are implemented using Python programming language (version 3.6) and Scikit-learn package (version 0.22).
    Python
    suggested: (IPython, SCR_001658)
          <div style="margin-bottom:8px">
        <div><b>Scikit-learn</b></div>
        <div>suggested: (scikit-learn, <a href="https://scicrunch.org/resources/Any/search?q=SCR_002577">SCR_002577</a>)</div>
      </div>
    </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">The metric aAcc was used to find the best prediction model.</td><td style="min-width:100px;border-bottom:1px solid lightgray">
      <div style="margin-bottom:8px">
        <div><b>aAcc</b></div>
        <div>suggested: (AACC, <a href="https://scicrunch.org/resources/Any/search?q=SCR_011999">SCR_011999</a>)</div>
      </div>
    </td></tr></table>

    Data from additional tools added to each annotation on a weekly basis.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.07.27.20044990v1 on medRxiv