Refining Boolean queries to identify relevant studies for systematic review updates
Year: 2020
Type: review
Abstract: OBJECTIVE: Systematic reviews are important in health care but are expensive to produce and maintain. The authors explore the use of automated transformations of Boolean queries to improve the identification of relevant studies for updates to systematic reviews. MATERIALS AND METHODS: A set of query transformations, including operator substitution, query expansion, and query reduction, were used to iteratively modify the Boolean query used for the original systematic review. The most effective transformation at each stage is identified using information about the studies included and excluded from the original review. A dataset consisting of 22 systematic reviews was used for evaluation. Updated queries were evaluated using the included and excluded studies from the updated version of the review. Recall and precision were used as evaluation measures. RESULTS: The updated queries were more effective than the ones used for the original review, in terms of both precision and recall. The overall number of documents retrieved was reduced by more than half, while the number of relevant documents found increased by 10.3%. CONCLUSIONS: Identification of relevant studies for updates to systematic reviews can be carried out more effectively by using information about the included and excluded studies from the original review to produce improved Boolean queries. These updated queries reduce the overall number of documents retrieved while also increasing the number of relevant documents identified, thereby representing a considerable reduction in effort required by systematic reviewers.
Authors: Amal Alharbi, Mark Stevenson
Institution: University of Sheffield
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3087993102