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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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TrialSieve: A Comprehensive Biomedical Information Extraction Framework for PICO, Meta-Analysis, and Drug

David Kartchner1, Haydn Turner1, Christophe Ye1

  • 1Laboratory for Pathology Dynamics, Georgia Institute of Technology, Emory University School of Medicine, Atlanta, GA 30332, USA.

Bioengineering (Basel, Switzerland)
|May 28, 2025
PubMed
Summary
This summary is machine-generated.

TrialSieve enhances biomedical information extraction for clinical meta-analysis and drug repurposing. Automated NLP models trained on its data can match or exceed human performance in annotation tasks.

Keywords:
artificial intelligencebiocurationbiomedical information extractionbiomedical literature annotationbiomedical literature schemalarge language modelnamed entity recognitionnatural-language processingtext mining

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Clinical Research

Background:

  • Traditional PICO (Patient, Intervention, Comparison, Outcome) methods lack quantitative comparison capabilities for clinical outcomes.
  • Biomedical information extraction is crucial for meta-analysis and drug repurposing but faces challenges with data complexity.
  • Existing annotation frameworks may not fully capture the nuances required for comprehensive systematic reviews.

Purpose of the Study:

  • Introduce TrialSieve, a novel framework for biomedical information extraction.
  • Enhance clinical meta-analysis and drug repurposing through improved data annotation and comparison.
  • Evaluate the performance of various NLP models and a large language model (LLM) using the TrialSieve dataset.

Main Methods:

  • Developed TrialSieve, incorporating hierarchical, treatment group-based graphs extending PICO.
  • Annotated 1609 PubMed abstracts with 20 categories, resulting in 170,557 annotations and 52,638 spans.
  • Evaluated NLP models (BioLinkBERT, BioBERT, KRISSBERT, PubMedBERT) and GPT-4o on the TrialSieve dataset for entity labeling.
  • Conducted an annotator user study (n=39) to assess the efficiency and accuracy of the TrialSieve annotation approach.

Main Results:

  • BioLinkBERT achieved the highest accuracy (0.875) and recall (0.679) in biomedical entity labeling.
  • PubMedBERT demonstrated the best precision (0.614) and F1-score (0.639).
  • NLP models trained on imperfectly annotated data matched or surpassed human performance, indicating feasibility of automation.
  • The TrialSieve tree-based approach significantly improved annotator efficiency and accuracy (p < 0.05).

Conclusions:

  • TrialSieve offers a robust foundation for automated biomedical information extraction.
  • The framework facilitates more comprehensive and quantitative comparisons for clinical meta-analysis and drug repurposing.
  • Automated information extraction using NLP models is feasible even with noisy, human-annotated datasets.