Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR)

被引:0
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作者
Elaine Beller
Justin Clark
Guy Tsafnat
Clive Adams
Heinz Diehl
Hans Lund
Mourad Ouzzani
Kristina Thayer
James Thomas
Tari Turner
Jun Xia
Karen Robinson
Paul Glasziou
机构
[1] Bond University,Centre for Research in Evidence
[2] Macquarie University,Based Practice
[3] University of Nottingham,Australian Institute of Health Innovation
[4] Bergen University College,Faculty of Medicine and Health Sciences
[5] Western Norway University of Applied Sciences,Centre for Evidence
[6] Hamad Bin Khalifa University,Based Practice
[7] PennState University,Qatar Computing Research Institute
[8] University College London,National Institute of Environmental Health Sciences
[9] Monash University,JHU Evidence
[10] Johns Hopkins University,based Practice Center
来源
Systematic Reviews | / 7卷
关键词
Systematic review; Automation; Collaboration;
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学科分类号
摘要
Systematic reviews (SR) are vital to health care, but have become complicated and time-consuming, due to the rapid expansion of evidence to be synthesised. Fortunately, many tasks of systematic reviews have the potential to be automated or may be assisted by automation. Recent advances in natural language processing, text mining and machine learning have produced new algorithms that can accurately mimic human endeavour in systematic review activity, faster and more cheaply. Automation tools need to be able to work together, to exchange data and results. Therefore, we initiated the International Collaboration for the Automation of Systematic Reviews (ICASR), to successfully put all the parts of automation of systematic review production together. The first meeting was held in Vienna in October 2015. We established a set of principles to enable tools to be developed and integrated into toolkits.
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