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Using text mining for study identification in systematic reviews: A systematic review of current approaches
Resource type
Journal Article
Authors/contributors
- O’Mara-Eves, Alison (Author)
- Thomas, James (Author)
- McNaught, John (Author)
- Miwa, Makoto (Author)
- Ananiadou, Sophia (Author)
Title
Using text mining for study identification in systematic reviews: A systematic review of current approaches
Abstract
The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming. Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved. The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic review fills that research gap. Focusing mainly on non-technical issues, the review aims to increase awareness of the potential of these technologies and promote further collaborative research between the computer science and systematic review communities.
Publication
Systematic Reviews
Volume
4
Issue
1
Pages
5
Date
2015-01-14
Journal Abbr
Systematic Reviews
ISSN
2046-4053
Short Title
Using text mining for study identification in systematic reviews
Accessed
19/01/2024, 22:00
Library Catalogue
BioMed Central
Citation
O’Mara-Eves, A., Thomas, J., McNaught, J., Miwa, M., & Ananiadou, S. (2015). Using text mining for study identification in systematic reviews: A systematic review of current approaches. Systematic Reviews, 4(1), 5. https://doi.org/10.1186/2046-4053-4-5
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