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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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SWIFT-Review: a text-mining workbench for systematic review.

Brian E Howard1, Jason Phillips2, Kyle Miller2

  • 1SciOme LLC, Research Triangle Park, 2 Davis Drive, 27709, NC, USA. brian.howard@sciome.com.

Systematic Reviews
|May 25, 2016
PubMed
Summary

Machine learning software like SWIFT-Review can significantly reduce the human effort in systematic reviews by prioritizing relevant studies. This tool also aids in identifying research gaps during the problem formulation stage.

Keywords:
Literature prioritizationSWIFT-ReviewScoping reportsSoftwareSystematic review

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Systematic review methodology

Background:

  • Growing interest in machine learning (ML) for systematic reviews.
  • Challenges in identifying research questions within large literature bases.
  • Need for efficient literature screening tools.

Purpose of the Study:

  • Assess the SWIFT-Review algorithm for priority ranking of studies.
  • Explore SWIFT-Review's utility in systematic review problem formulation.
  • Identify and visualize data-rich/data-poor research areas.

Main Methods:

  • Evaluated SWIFT-Review's ranking performance on 20 case studies (15 public datasets).
  • Used machine training with manually annotated seed sets.
  • Ranked references using term frequency and LDA topic modeling.
  • Assessed performance using "studies screened" and "Work Saved over Sampling" (WSS) metrics.
  • Utilized SWIFT-Review for categorizing and visualizing 264,588 studies on EDCs.

Main Results:

  • SWIFT-Review achieved top performance scores on 11 out of 15 public datasets.
  • ML-driven document triage can save over 50% of screening effort.
  • SWIFT-Review's tagging and annotation features support scoping and problem formulation.

Conclusions:

  • Text-mining and ML software, such as SWIFT-Review, are valuable for reducing screening burden.
  • SWIFT-Review assists in the problem formulation phase of systematic reviews.