Extending the range of symptoms in a Bayesian Network for the Predictive Diagnosis of COVID-19

  1. Rachel Butcher
  2. Norman Fenton

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Abstract

Emerging digital technologies have taken an unprecedented position at the forefront of COVID-19 management. This paper extends a previous Bayesian network designed to predict the probability of COVID-19 infection, based on a patient’s profile. The structure and prior probabilities have been amalgamated from the knowledge of peer-reviewed articles. The network accounts for demographics, behaviours and symptoms, and can mathematically identify multivariate combinations with the highest risk. Potential applications include patient triage in healthcare systems or embedded software for contact-tracing apps. Specifically, this paper extends the set of symptoms that are a marker for COVID-19 infection and the differential diagnosis of other conditions with similar presentations.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.22.20217554: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableLocations include Henan, Zhejiang and Wuhan in China, California, Spain, Tokyo, and Daegu in Korea; with a total of 4062 male patients and 4033 female patients (Table. 1).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The database is updated daily with all publicly available COVID-19 research, including preprints, amalgamated from the following publishers: Medline, PubMed Central, Embase, CAB Abstracts, Global Health, PsycInfo, Cochrane Library, Scopus, Academic Search Complete, Africa Wide Information, CINAHL, ProQuest Central, SciFinder, the Virtual Health Library, LitCovid, WHO COVID-19 website, CDC COVID-19 website, Eurosurveillance, China CDC Weekly, Homeland Security Digital Library, ClinicalTrials.gov, bioRxiv, medRxiv, chemRxiv, and SSRN (Centers for Disease Control and Prevention, 2020).
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    PsycInfo
    suggested: (PsycINFO, RRID:SCR_014799)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

  2. Version published to 10.1101/2020.10.22.20217554v1 on medRxiv