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IK-DDI: a novel framework based on instance position embedding and key external text for DDI extraction.

Mingliang Dou1, Jiaqi Ding2, Genlang Chen3

  • 1College of Intelligence and Computing, Tianjin University, 300354, Tianjin, China.

Briefings in Bioinformatics
|March 18, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces IK-DDI, a novel framework for extracting drug-drug interactions (DDIs) from scientific texts. IK-DDI improves accuracy by considering instance connections and effectively utilizing external data for enhanced pharmacovigilance.

Keywords:
DDI extractiondrug matchinginstance position embeddingkey external text

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Area of Science:

  • Pharmacovigilance and Drug Safety
  • Biomedical Natural Language Processing
  • Computational Pharmacology

Background:

  • Drug-drug interactions (DDIs) are critical for public health and pharmacovigilance.
  • Extracting DDIs from scientific literature offers a cost-effective alternative to clinical trials.
  • Current DDI extraction methods often overlook inter-instance relationships and struggle with external data integration.

Purpose of the Study:

  • To propose a novel framework, IK-DDI, for improved DDI extraction from scientific articles.
  • To enhance DDI prediction accuracy by leveraging instance position information and external text data.
  • To address limitations in current methods regarding inter-instance connections and external data utilization.

Main Methods:

  • Developed the IK-DDI framework incorporating instance position embedding (article-level and sentence-level).
  • Implemented a comprehensive similarity-matching method (string and word sense) for target drug-external text alignment.
  • Utilized a key sentence search method to extract relevant information from external data sources.

Main Results:

  • The IK-DDI framework demonstrated superior performance over existing methods on both macro-averaged and micro-averaged metrics.
  • The integration of instance position information strengthened connections between related DDI instances.
  • Improved accuracy in matching target drugs with external text data was achieved.

Conclusions:

  • IK-DDI provides a robust framework for extracting biomedical entity relationships, particularly DDIs.
  • The method effectively utilizes inter-instance connections and external text data for enhanced extraction efficiency.
  • This approach offers a significant advancement in processing external text data for pharmacovigilance.