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SWIFT-Review: a text-mining workbench for systematic review

Overview of attention for article published in Systematic Reviews, January 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

24 tweeters


48 Dimensions

Readers on

179 Mendeley
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SWIFT-Review: a text-mining workbench for systematic review
Published in
Systematic Reviews, January 2016
DOI 10.1186/s13643-016-0263-z
Pubmed ID

Brian E. Howard, Jason Phillips, Kyle Miller, Arpit Tandon, Deepak Mav, Mihir R. Shah, Stephanie Holmgren, Katherine E. Pelch, Vickie Walker, Andrew A. Rooney, Malcolm Macleod, Ruchir R. Shah, Kristina Thayer


There is growing interest in using machine learning approaches to priority rank studies and reduce human burden in screening literature when conducting systematic reviews. In addition, identifying addressable questions during the problem formulation phase of systematic review can be challenging, especially for topics having a large literature base. Here, we assess the performance of the SWIFT-Review priority ranking algorithm for identifying studies relevant to a given research question. We also explore the use of SWIFT-Review during problem formulation to identify, categorize, and visualize research areas that are data rich/data poor within a large literature corpus. Twenty case studies, including 15 public data sets, representing a range of complexity and size, were used to assess the priority ranking performance of SWIFT-Review. For each study, seed sets of manually annotated included and excluded titles and abstracts were used for machine training. The remaining references were then ranked for relevance using an algorithm that considers term frequency and latent Dirichlet allocation (LDA) topic modeling. This ranking was evaluated with respect to (1) the number of studies screened in order to identify 95 % of known relevant studies and (2) the "Work Saved over Sampling" (WSS) performance metric. To assess SWIFT-Review for use in problem formulation, PubMed literature search results for 171 chemicals implicated as EDCs were uploaded into SWIFT-Review (264,588 studies) and categorized based on evidence stream and health outcome. Patterns of search results were surveyed and visualized using a variety of interactive graphics. Compared with the reported performance of other tools using the same datasets, the SWIFT-Review ranking procedure obtained the highest scores on 11 out of 15 of the public datasets. Overall, these results suggest that using machine learning to triage documents for screening has the potential to save, on average, more than 50 % of the screening effort ordinarily required when using un-ordered document lists. In addition, the tagging and annotation capabilities of SWIFT-Review can be useful during the activities of scoping and problem formulation. Text-mining and machine learning software such as SWIFT-Review can be valuable tools to reduce the human screening burden and assist in problem formulation.

Twitter Demographics

The data shown below were collected from the profiles of 24 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 179 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United Arab Emirates 1 <1%
Denmark 1 <1%
Canada 1 <1%
Unknown 175 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 36 20%
Researcher 32 18%
Student > Ph. D. Student 25 14%
Librarian 16 9%
Other 11 6%
Other 36 20%
Unknown 23 13%
Readers by discipline Count As %
Computer Science 36 20%
Medicine and Dentistry 31 17%
Agricultural and Biological Sciences 17 9%
Psychology 8 4%
Engineering 7 4%
Other 48 27%
Unknown 32 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 November 2018.
All research outputs
of 16,750,089 outputs
Outputs from Systematic Reviews
of 1,514 outputs
Outputs of similar age
of 269,559 outputs
Outputs of similar age from Systematic Reviews
of 16 outputs
Altmetric has tracked 16,750,089 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,514 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.8. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 269,559 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.