Research Output
Building a Systematic Online Living Evidence Summary of COVID-19 Research
  Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence.

  • Type:

    Article

  • Date:

    24 June 2021

  • Publication Status:

    Published

  • Publisher

    European Association for Health Information and Libraries EAHIL

  • DOI:

    10.32384/jeahil17465

  • Cross Ref:

    10.32384/jeahil17465

  • ISSN:

    1841-0715

  • Funders:

    Edinburgh Napier Funded

Citation

Hair, K., Sena, E. S., Wilson, E., Currie, G., Macleod, M., Bahor, Z., …Drury, B. (in press). Building a Systematic Online Living Evidence Summary of COVID-19 Research. Journal of the European Association for Health Information and Libraries, 17(2), 21-26. https://doi.org/10.32384/jeahil17465

Authors

Keywords

COVID-19; evidence synthesis; machine learning; web application; database

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