UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Pinpointing needles in giant haystacks: use of text mining to reduce impractical screening workload in extremely large scoping reviews

Shemilt, I; Simon, A; Hollands, GJ; Marteau, TM; Ogilvie, D; O'Mara-Eves, A; Kelly, MP; (2014) Pinpointing needles in giant haystacks: use of text mining to reduce impractical screening workload in extremely large scoping reviews. Research Synthesis Methods , 5 (1) pp. 31-49. 10.1002/jrsm.1093. Green open access

[thumbnail of Giant haystacks_2013.pdf]
Preview
Text
Giant haystacks_2013.pdf - Published Version

Download (1MB) | Preview

Abstract

In scoping reviews, boundaries of relevant evidence may be initially fuzzy, with refined conceptual understanding of interventions and their proposed mechanisms of action an intended output of the scoping process rather than its starting point. Electronic searches are therefore sensitive, often retrieving very large record sets that are impractical to screen in their entirety. This paper describes methods for applying and evaluating the use of text mining (TM) technologies to reduce impractical screening workload in reviews, using examples of two extremely large-scale scoping reviews of public health evidence (choice architecture (CA) and economic environment (EE)). Electronic searches retrieved >800,000 (CA) and >1 million (EE) records. TM technologies were used to prioritise records for manual screening. TM performance was measured prospectively. TM reduced manual screening workload by 90% (CA) and 88% (EE) compared with conventional screening (absolute reductions of ≈430 000 (CA) and ≈378 000 (EE) records). This study expands an emerging corpus of empirical evidence for the use of TM to expedite study selection in reviews. By reducing screening workload to manageable levels, TM made it possible to assemble and configure large, complex evidence bases that crossed research discipline boundaries. These methods are transferable to other scoping and systematic reviews incorporating conceptual development or explanatory dimensions.

Type: Article
Title: Pinpointing needles in giant haystacks: use of text mining to reduce impractical screening workload in extremely large scoping reviews
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/jrsm.1093
Publisher version: http://dx.doi.org/10.1002/jrsm.1093
Language: English
Additional information: Copyright © 2013 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Keywords: scoping review methods, study selection, systematic review methods, text mining, Data Mining, Machine Learning, Natural Language Processing, Pattern Recognition, Automated, Periodicals as Topic, Review Literature as Topic, Vocabulary, Controlled, Workload
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Social Research Institute
URI: https://discovery.ucl.ac.uk/id/eprint/1475803
Downloads since deposit
138Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item