Using automation to produce a ‘living map’ of the COVID-19 research literature

  • Ian Shemilt
  • Anneliese Arno
  • James Thomas EPPI Centre, UCL
  • Theo Lorenc
  • Claire Khouja
  • Gary Raine
  • Katy Sutcliffe
  • Preethy D'Souza
  • Kath Wright
  • Amanda Sowden


The COVID-19 pandemic has disrupted life worldwide and presented unique challenges in the health evidencesynthesis space. The urgent nature of the pandemic required extreme rapidity for keeping track of research, andthis presented a unique opportunity for long-proposed automation systems to be deployed and evaluated. Wecompared the use of novel automation technologies with conventional manual screening; and Microsoft AcademicGraph (MAG) with the MEDLINE and Embase databases locating the emerging research evidence. We foundthat a new workflow involving machine learning to identify relevant research in MAG achieved a much higherrecall with lower manual effort than using conventional approaches.
How to Cite
Shemilt I, Arno A, Thomas J, Lorenc T, Khouja C, Raine G, Sutcliffe K, D’Souza P, Wright K, Sowden A. Using automation to produce a ‘living map’ of the COVID-19 research literature. JEAHIL [Internet]. 23Jun.2021 [cited 3Dec.2023];17(2):11-5. Available from:
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