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Aligning text mining and machine learning algorithms with best practices for study selection in systematic literature

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Systematic Reviews
|December 14, 2020
PubMed
Summary

Machine learning (ML) algorithms enhance systematic literature reviews (SLRs) for health technology assessment (HTA) by identifying exclusions with reasons. Optimized ML models improve efficiency and reviewer workload, aligning with HTA guidance.

Keywords:
ClassificationDownsamplingMachine learningMethodsReasons for exclusionStudy selectionSystematic literature reviewsText miningUpdates

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Area of Science:

  • Health Technology Assessment (HTA)
  • Systematic Literature Review (SLR)
  • Machine Learning (ML)

Background:

  • The application of ML in SLRs for HTA is not well-established, lacking extensive testing and agency guidance.
  • Existing research focuses on text mining and ML for screening, but not specifically for HTA workflows.
  • Two key knowledge gaps addressed: extending ML to provide exclusion reasons and optimizing ML parameters.

Purpose of the Study:

  • To extend ML algorithms to provide reasons for citation exclusion, aligning with HTA practices.
  • To determine optimal parameter settings for feature-set generation and ML algorithms in SLRs.
  • To evaluate the performance of ML algorithms in the context of HTA.

Main Methods:

  • Utilized data from five large SLRs (3089-12,769 abstracts) across diverse disease areas.
  • Developed a multi-step algorithm categorizing citations (included, excluded by PICOS, unclassified) using a bag-of-words approach.
  • Compared ML algorithms (SVM, Naïve Bayes, CART) and training strategies (full data vs. downsampling, abstract vs. full-text decisions).

Main Results:

  • The optimal model used SVM with full-text decisions, downsampling, and word frequency filtering (min. 5 occurrences).
  • Achieved high sensitivity (94-100%) and specificity (54-89%) across SLRs.
  • Successfully provided reasons for exclusion for 75% of excluded citations, matching reviewer decisions in 83% of cases. Sensitivity improved with downsampling and abstract decisions.

Conclusions:

  • ML algorithms can significantly improve SLR efficiency for HTA by identifying exclusions with relevant PICOS reasons.
  • The proposed ML approach can reduce the workload for secondary reviewers, aligning with HTA guidance.
  • Downsampling and full-text exclusion decisions enhance study selection and support a 'learn-as-you-go' methodology.