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Improved relation extraction using BERT and Edge sampling enhances adverse drug event identification. These methods significantly reduce errors in identifying drug-related medical problems and indications from biomedical texts.

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Pharmacovigilance

Background:

  • Accurate relation extraction from biomedical text is crucial for clinical decision support.
  • Identifying Adverse Drug Events (ADE) and their causes is a key challenge in pharmacovigilance.
  • Existing methods show promise but have room for performance improvement.

Purpose of the Study:

  • To evaluate the effectiveness of Bidirectional Encoder Representations from Transformers (BERT) for relation extraction.
  • To assess the impact of Edge sampling for negative training sample selection.
  • To improve the extraction of Adverse Drug Events (ADE) and Reason (indication) relations.

Main Methods:

  • Utilized the state-of-the-art deep learning model BERT for contextual representations.
  • Implemented Edge sampling based on the "near-miss" hypothesis for negative sample selection.
  • Evaluated performance on the MADE and N2C2 Task-2 datasets.

Main Results:

  • BERT and Edge sampling combined improved ADE and Reason relation extraction by 6.4-6.7 absolute percentage points.
  • This combination led to a 24%-28% reduction in error rates for ADE and Reason relations.
  • Performance gains were particularly notable for relations with longer text spans between entities.

Conclusions:

  • BERT and Edge sampling offer a significant advancement in biomedical relation extraction, especially for ADE and indication identification.
  • The synergistic effect of BERT and Edge sampling effectively handles longer contextual information.
  • These methods provide a substantial reduction in error rates, enhancing pharmacovigilance capabilities.