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Towards semantic-driven boolean query formalization for biomedical systematic literature reviews.

Mohammadreza Pourreza1, Faezeh Ensan2

  • 1Department of Computer Engineering Ferdowsi University of Mashhad, Mashhad, Iran.

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|November 28, 2022
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Summary

This study introduces an automated approach to generate search queries for biomedical literature reviews, significantly improving efficiency and accuracy. The new method outperforms existing models and can even match manual query performance for study identification.

Keywords:
Biomedical contextual embeddingBoolean query formalizationSystematic reviewTechnology-aided reviewsUnified medical language system

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Systematic Review Methodology

Background:

  • Manual construction of search queries for systematic reviews is time-consuming and labor-intensive.
  • Effective study identification is crucial for retrieving all relevant evidence for systematic reviews.
  • Existing automatic query generation methods require improvement in performance and efficiency.

Approach:

  • Utilized pre-trained language models to generate key-phrases and dense embeddings from abstracts and keywords.
  • Developed a large dataset of nearly one million PubMed article abstracts and keywords for model fine-tuning.
  • Integrated Unified Medical Language System (UMLS) concepts for query expansion and embedding generation.
  • Applied clustering methods (Agglomerative, Affinity Propagation, K-Means) to generated embeddings for query clause formation.

Key Points:

  • The proposed automatic query generation significantly outperforms state-of-the-art models in Precision, Recall, and F-measures.
  • Achieved a Precision of 0.0821 (vs. 0.005), Recall of 0.9676 (vs. 0.878), and F3-measure of 0.2898 (vs. 0.0356).
  • Some methods demonstrated performance comparable to or exceeding manually crafted queries.

Conclusions:

  • The developed model provides an effective initial search query for systematic reviews, requiring human refinement.
  • Future work will explore integrating these methods into existing study identification techniques, incorporating expert feedback.
  • This approach has the potential to reduce costs and accelerate the systematic review process.