(untitled) (Journal of EAHIL, 2021)
The COVID-19 pandemic has disrupted life worldwide and presented unique challenges in the health evidence synthesis space. The urgent nature of the pandemic required extreme rapidity for keeping track of research, and this presented a unique opportunity for long-proposed automation systems to be deployed and evaluated. We compared the use of novel automation technologies with conventional manual screening; and Microsoft Academic Graph (MAG) with the MEDLINE and Embase databases locating the emerging research evidence. We found that a new workflow involving machine learning to identify relevant research in MAG achieved a much higher recall with lower manual effort than using conventional approaches.