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Related Experiment Video

Updated: Dec 25, 2025

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SWIFT-Active Screener: Accelerated document screening through active learning and integrated recall estimation.

Brian E Howard1, Jason Phillips1, Arpit Tandon1

  • 1Sciome LLC, 2 Davis Drive Durham, NC 27709, USA.

Environment International
|March 24, 2020
PubMed
Summary
This summary is machine-generated.

SWIFT-Active Screener significantly reduces systematic review screening time by using machine learning to prioritize articles. This tool achieves 95% recall after screening only 40% of references, saving substantial labor for researchers.

Keywords:
Active learningDocument screeningEvidence mappingMachine learningRecall estimationSystematic review

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Area of Science:

  • Bibliometrics
  • Information Science
  • Health Informatics

Background:

  • Systematic reviews require extensive article screening using strict inclusion/exclusion criteria.
  • Screening thousands of articles can demand hundreds of person-hours, representing a significant labor burden.

Purpose of the Study:

  • Introduce SWIFT-Active Screener, a web-based software to decrease the workload in systematic review screening.
  • Employ active learning and a negative binomial model to prioritize articles and estimate remaining relevant documents.

Main Methods:

  • Utilized active learning, incorporating user feedback to prioritize articles for review.
  • Implemented a negative binomial model for estimating the quantity of relevant articles yet to be screened.
  • Evaluated prioritization and recall estimation using simulations on 26 diverse systematic review datasets.

Main Results:

  • Achieved 95% recall of relevant articles after screening an average of 40% of the total references.
  • For large datasets (≥5,000 references), 95% recall was met after screening only 34% of references.
  • The proposed recall estimator provided a conservative and useful estimation of identified relevant documents.

Conclusions:

  • SWIFT-Active Screener offers substantial time savings over traditional screening methods, especially for large-scale reviews.
  • The integrated recall estimation addresses the critical question of when to cease screening in machine learning-assisted reviews.
  • The collaborative, online web application is readily available for multi-user systematic review projects.