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A Text-Mining Framework for Supporting Systematic Reviews.

Dingcheng Li1,2, Zhen Wang1,3, Liwei Wang1

  • 1Department of Health Sciences Research, Mayo Clinic, Rochester, USA.

American Journal of Information Management
|October 27, 2017
PubMed
Summary
This summary is machine-generated.

Advanced text-mining tools significantly reduce abstract screening workload in systematic reviews (SRs). This approach saves labor while maintaining high recall, offering an efficient summary of relevant studies.

Keywords:
Data MiningIndexed-Term RelevanceKeyword RelevanceSystematic ReviewText MiningTopic ModelingTopic Relevance

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Area of Science:

  • Bibliometrics
  • Information Science
  • Computational Linguistics

Background:

  • Systematic reviews (SRs) require rigorous identification, appraisal, and synthesis of studies.
  • High-quality SRs are time-consuming and resource-intensive, particularly the abstract screening phase.
  • Current methods for abstract screening in SRs present a significant bottleneck.

Purpose of the Study:

  • To investigate advanced text-mining approaches for reducing abstract screening burden in SRs.
  • To develop and evaluate a text-mining framework for SR abstract screening and information summary.
  • To assess the efficiency and effectiveness of semantics-based ranking metrics in SR workflows.

Main Methods:

  • Proposed a text-mining framework with three semantics-based ranking metrics: keyword relevance, indexed-term relevance, and topic relevance.
  • Keyword relevance utilized user-defined search strategy keywords.
  • Indexed-term relevance leveraged expert-developed vocabulary, while topic relevance used latent Dirichlet allocation for semantic similarity analysis.

Main Results:

  • The framework achieved 100% recall in three diverse SRs (Mass Media Interventions, Rectal Cancer, Influenza Vaccine).
  • Significant abstract screening labor savings were observed: 91.8%, 85.7%, and 49.3% respectively.
  • Manual identification of relevant studies confirmed strong topic similarity, validating topic analysis as a key metric.

Conclusions:

  • Advanced text-mining approaches can substantially decrease the labor involved in SR abstract screening.
  • The proposed framework effectively reduces screening workload while ensuring comprehensive identification of relevant studies.
  • Text mining offers a viable solution for streamlining SR processes and providing informative study summaries.