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Related Experiment Video

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Text mining to support abstract screening for knowledge syntheses: a semi-automated workflow.

Ba' Pham1, Jelena Jovanovic2, Ebrahim Bagheri3

  • 1Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, Ontario, M5B 1T8, Canada.

Systematic Reviews
|May 27, 2021
PubMed
Summary

This study introduces a semi-automated workflow for abstract screening in systematic reviews, enhancing efficiency and accuracy. The developed workflow significantly reduces workload while maintaining high sensitivity and precision in identifying eligible studies.

Keywords:
Abstract screeningAutomationClassification modelMachine learningNatural language processingScoping reviewSystematic reviewText mining

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Area of Science:

  • Information Science
  • Biomedical Informatics
  • Health Sciences Research

Background:

  • Current text mining tools for abstract screening in systematic reviews often lack sufficient sensitivity and precision.
  • There is a need for accessible, semi-automated workflows to improve abstract screening efficiency in knowledge synthesis.

Purpose of the Study:

  • To develop and evaluate an accessible, semi-automated workflow for abstract screening in systematic reviews and other knowledge synthesis methods.
  • To improve the sensitivity, precision, and efficiency of abstract screening compared to traditional methods.

Main Methods:

  • Utilized text-mining and machine-learning to process abstracts into numerical data and train a classification model.
  • Implemented an iterative process where human reviewers validate predicted abstracts, and newly identified eligible abstracts refine the model.
  • Employed near-neighbor methods to identify similar abstracts for screening and updated the classification model iteratively.
  • Evaluated the workflow using a systematic review of insulin formulations and a scoping review of knowledge-synthesis methods, comparing performance against dual-reviewer screening.

Main Results:

  • The workflow achieved high performance metrics: 88%-89% sensitivity, 99% specificity, 71%-72% precision, and 79% F1-score.
  • Demonstrated significant workload reduction (55%-63%) and a decrease in abstracts requiring full-text retrieval (11%-12%).
  • Identified all eligible studies in one review and missed only 1.5% in another, with minimal impact on conclusions.

Conclusions:

  • The semi-automated workflow offers a sensitive, precise, and efficient alternative to manual abstract screening by two reviewers.
  • The workflow is accessible to reviewers with limited natural language processing or machine learning experience.
  • The developed workflow and its code are available to facilitate broader adoption in systematic reviews and knowledge synthesis.