作者
Kaitlyn Hair, Emily S Sena, Emma Wilson, Gillian Currie, Malcolm Macleod, Zsanett Bahor, Chris Sena, Can Ayder, Jing Liao, Ezgi Tanriver Ayder, Joly Ghanawi, Anthony Tsang, Anne Collins, Alice Carstairs, Sarah Antar, Katie Drax, Kleber Neves, Thomas Ottavi, Yoke Yue Chow, David Henry, Cigdem Selli, Mariam Fofana, Martina Rudnicki, Brendan Gabriel, Esther J Pearl, Simran S Kapoor, Julija Baginskaite, Santosh Shevade, Alexandria Chung, Marianna Antonia Przybylska, David E Henshall, Karina Lôbo Hajdu, Sarah McCann, Catherine Sutherland, Tiago Lubiana Alves, Rachel Blacow, Rebecca J Hood, Nadia Soliman, Alison Harris, Stephanie L Swift, Torsten Rackoll, Nathalie Percie du Sert, Fergal Waldron, Magnus Macleod, Ruth Moulson, Juin W Low, Kristiina Rannikmae, Kirsten Miller, Alexandra Bannach-Brown, Fiona Kerr, Harry L Hébert, Sarah Gregory, Isaac William Shaw, Alexander Christides, Mohammed Alawady, Robert Hillary, Alex Clark, Natasha Jayasuriya, Samantha Sives, Ahmed Nazzal, Nimesh Jayasuriya, Michael Sewell, Rita Bertani, Helen Fielding, Broc Drury
发表日期
2021/6/24
期刊
Journal of EAHIL
卷号
17
期号
2
页码范围
21-26
简介
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.
学术搜索中的文章
K Hair, ES Sena, E Wilson, G Currie, M Macleod… - Journal of EAHIL, 2021