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Refining Boolean queries to identify relevant studies for systematic review updates.

Amal Alharbi1, Mark Stevenson1

  • 1Computer Science Department, University of Sheffield, Sheffield, United Kingdom.

Journal of the American Medical Informatics Association : JAMIA
|October 17, 2020
PubMed
Summary
This summary is machine-generated.

Automated Boolean query transformations improve systematic review updates. This method reduces retrieved documents by over half while increasing relevant findings by 10.3%, saving reviewer effort.

Keywords:
lexical statisticsquery reformulationscreeningsystematic reviewssystematic reviews updates

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Area of Science:

  • Health Informatics
  • Information Science
  • Evidence-Based Medicine

Background:

  • Systematic reviews are crucial for healthcare but are resource-intensive to produce and maintain.
  • Identifying relevant studies for review updates is a significant challenge.

Purpose of the Study:

  • To explore automated transformations of Boolean queries for enhancing study identification in systematic review updates.
  • To reduce the cost and effort associated with maintaining systematic reviews.

Main Methods:

  • Iterative modification of Boolean queries using operator substitution, query expansion, and query reduction.
  • Utilizing included/excluded study data from original reviews to identify optimal query transformations.
  • Evaluating updated queries on a dataset of 22 systematic reviews using precision and recall metrics.

Main Results:

  • Updated Boolean queries significantly improved both precision and recall compared to original queries.
  • The number of retrieved documents was reduced by over 50%.
  • The number of relevant documents identified increased by 10.3%.

Conclusions:

  • Automated Boolean query refinement using original review data enhances the efficiency of identifying relevant studies for systematic review updates.
  • This approach reduces the overall document retrieval volume and increases the yield of relevant studies, thereby decreasing reviewer workload.