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Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning

Yan Hu1, Vipina K Keloth2, Kalpana Raja2

  • 1School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77054, United States.

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This study introduces a two-step natural language processing (NLP) pipeline for extracting Participants, Interventions, Comparisons, and Outcomes (PICO) from randomized controlled trial abstracts. The method significantly improves PICO element extraction accuracy for evidence synthesis.

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Area of Science:

  • Medical Informatics
  • Computational Linguistics

Background:

  • Automated extraction of PICO (Participants, Intervention, Comparison, Outcome) elements from randomized controlled trial (RCT) abstracts is crucial for efficient evidence synthesis.
  • Previous natural language processing (NLP) approaches for PICO extraction face limitations due to the complexity of RCT abstracts and annotation challenges.

Purpose of the Study:

  • To develop and evaluate a robust two-step NLP pipeline for accurate PICO element extraction from RCT abstracts.
  • To enhance the efficiency and effectiveness of evidence synthesis by improving automated PICO information retrieval.

Main Methods:

  • A two-step NLP pipeline was proposed, involving sentence classification using a prompt-based learning model followed by PICO extraction using a named entity recognition (NER) model.
  • Sentences were categorized into background, methods, results, and conclusions, with PICO extraction focused on title and methods sections containing over 96% of PICO information.
  • The pipeline was evaluated on three datasets: EBM-NLPmod, COVID-19 RCT abstracts, and Alzheimer's disease (AD) RCT abstracts.

Main Results:

  • The NLP pipeline achieved high performance across datasets, with token-level micro F1 scores of 0.833 (EBM-NLPmod), 0.928 (COVID-19), and 0.899 (AD).
  • Entity-level micro F1 scores were also strong, reaching 0.712 (EBM-NLPmod), 0.850 (COVID-19), and 0.805 (AD).
  • The proposed method demonstrates significant improvements in PICO extraction accuracy compared to previous approaches.

Conclusions:

  • The developed two-step NLP pipeline effectively extracts PICO elements from RCT abstracts, offering a valuable tool for evidence synthesis.
  • The approach shows high accuracy and generalizability across different medical domains, including infectious diseases and neurodegenerative disorders.
  • Publicly available code and datasets facilitate further research and application of this PICO extraction methodology.