Extending the range of symptoms in a Bayesian Network for the Predictive Diagnosis of COVID-19
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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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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 Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable Locations 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 Sentences Resources 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, … 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 Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable Locations 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 Sentences Resources 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). Medlinesuggested: (MEDLINE, RRID:SCR_002185)Embasesuggested: (EMBASE, RRID:SCR_001650)PsycInfosuggested: (PsycINFO, RRID:SCR_014799)Cochrane Librarysuggested: (Cochrane Library, RRID:SCR_013000)bioRxivsuggested: (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.
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