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Reducing systematic review workload through certainty-based screening.

Makoto Miwa1, James Thomas2, Alison O'Mara-Eves2

  • 1The National Centre for Text Mining and School of Computer Science, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK; Toyota Technological Institute, 2-12-1 Hisakata, Tempaku-ku, Nagoya 468-8511, Japan.

Journal of Biomedical Informatics
|June 24, 2014
PubMed
Summary
This summary is machine-generated.

Active learning effectively screens studies in systematic reviews, even for complex social science topics. The certainty criterion and weighting positive instances improve results, with unsupervised methods like Latent Dirichlet Allocation (LDA) offering further enhancement.

Keywords:
Active learningCertaintySystematic reviewsText mining

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Area of Science:

  • Information Science
  • Social Science Research
  • Medical Informatics

Background:

  • Systematic reviews face increasing screening workloads due to numerous publications.
  • Active learning (AL) shows potential for automating screening but needs broader discipline evaluation.
  • Its application to complex social science topics and data imbalance issues remain underexplored.

Purpose of the Study:

  • To evaluate the effectiveness of active learning (AL) in systematic reviews across clinical medicine and social science.
  • To assess the utility of certainty and uncertainty criteria for AL document selection.
  • To investigate enhancements for addressing data imbalance and improving AL performance.

Main Methods:

  • Applied active learning with certainty and uncertainty criteria in clinical medicine and public health (social science).
  • Incorporated several enhancements, including weighting positive instances and Latent Dirichlet Allocation (LDA).
  • Compared AL performance across different disciplines and data scenarios.

Main Results:

  • The certainty criterion proved effective for identifying relevant documents in both fields.
  • Weighting positive instances showed promise in mitigating data imbalance issues.
  • Latent Dirichlet Allocation (LDA) demonstrated potential benefits, especially with limited manual annotations.
  • Active learning is effective for complex topics, though text classification challenges limit efficiency.

Conclusions:

  • Active learning is a viable strategy for complex systematic review topics, including social sciences.
  • The certainty criterion and positive instance weighting are robust enhancements applicable across disciplines.
  • Unsupervised methods like LDA can improve AL performance without requiring additional manual annotation.