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

Hair, Kaitlyn; Sena, Emily S.; Wilson, Emma; Currie, Gillian; Macleod, Malcolm; Bahor, Zsanett; Sena, Chris; Ayder, Can; Liao, Jing; Tanriver Ayder, Ezgi; Ghanawi, Joly; Tsang, Anthony; Collins, Anne; Carstairs, Alice; Antar, Sarah; Drax, Katie; Neves, Kleber; Ottavi, Thomas; Chow, Yoke Yue; Henry, David; Selli, Cigdem; Fofana, Mariam; Rudnicki, Martina; Gabriel, Brendan; Pearl, Esther J; Kapoor, Simran S; Baginskaite, Julija; Shevade, Santosh; Chung, Alexandria; Przybylska, Marianna Antonia; Henshall, David E; Hajdu, Karina L?bo; McCann, Sarah; Sutherland, Catherine; Lubiana Alves, Tiago; Blacow, Rachel; Hood, Rebecca J.; Soliman, Nadia; Harris, Alison; Swift, Stephanie L.; Rackoll, Torsten; Percie du Sert, Nathalie; Waldron, Fergal; Macleod, Magnus; Moulson, Ruth; Low, Juin W.; Rannikmae, Kristiina; Miller, Kirsten; Bannach-Brown, Alexandra; Kerr, Fiona; H?bert, Harry L; Gregory, Sarah; Shaw, Isaac William; Christides, Alexander; Alawady, Mohammed; Hillary, Robert; Clark, Alex; Jayas...

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.

Citation

Hair, K., Sena, E. S., Wilson, E., Currie, G., Macleod, M., Bahor, Z., …Drury, B. (2021). 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

• Journal Article Type: Article
• Acceptance Date: Jun 10, 2021
• Online Publication Date: Jun 24, 2021
• Publication Date: 2021
• Deposit Date: Jul 12, 2021
• Publicly Available Date: Jul 12, 2021
• Journal: Journal of EAHIL
• Print ISSN: 1841-0715
• Publisher: European Association for Health Information and Libraries
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 2
• Pages: 21-26
• DOI: https://doi.org/10.32384/jeahil17465
• Keywords: COVID-19; evidence synthesis; machine learning; web application; database
• Public URL: http://researchrepository.napier.ac.uk/Output/2786196