Building a Systematic Online Living Evidence Summary of COVID-19 Research

the CAMARADES COVID-SOLES group

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)21-26
Number of pages6
JournalJournal of EAHIL
Volume17
Issue number2
DOIs
Publication statusPublished - 24 Jun 2021

Keywords

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

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