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Automating data extraction in systematic reviews: a systematic review.

Siddhartha R Jonnalagadda1, Pawan Goyal2, Mark D Huffman3

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Automating data extraction for systematic reviews is promising but limited. Current methods extract few data elements, with most achieving over 70% accuracy, yet a unified framework is missing.

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Area of Science:

  • Information Science
  • Biomedical Informatics
  • Natural Language Processing

Background:

  • Automating data extraction in systematic reviews can significantly reduce completion time.
  • The current state of automated data extraction from full texts requires thorough investigation.
  • This study systematically reviews existing methods for automating data extraction in systematic reviews.

Purpose of the Study:

  • To systematically review published and unpublished methods for automating data extraction in systematic reviews.
  • To assess the current landscape and limitations of automated data extraction techniques for systematic reviews.

Main Methods:

  • Systematic literature search of PubMed, IEEEXplore, and ACM Digital Library.
  • Inclusion criteria focused on methods/results describing extractable entities and automated extraction with evaluation.
  • Citation review of included reports to identify additional relevant studies.

Main Results:

  • 26 reports described automated extraction of over 52 potential data elements.
  • Automated extraction attempts were made for 48% of data elements, with 27% fully extracted.
  • The maximum number of data elements extracted in a single study was 7, with most achieving F-scores >70%.

Conclusions:

  • No unified information extraction framework exists for systematic reviews.
  • Published methods focus on a limited number of data elements (1-7).
  • Biomedical natural language processing techniques are underutilized for automating systematic review data extraction.