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An annotated corpus from biomedical articles to construct a drug-food interaction database.

Siun Kim1, Yoona Choi1, Jung-Hyun Won2

  • 1Department of Applied Biomedical Engineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea; Center for Convergence Approaches in Drug Development, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.

Journal of Biomedical Informatics
|January 10, 2022
PubMed
Summary

A new Drug-Food Interaction (DFI) corpus was created to aid DFI detection. This large, annotated dataset facilitates natural language processing systems for improved drug safety.

Keywords:
Biomedical corporaDrug interactionDrug-food interactionInformation extractionNatural language processing

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Area of Science:

  • Pharmacovigilance
  • Biomedical Natural Language Processing
  • Computational Linguistics

Background:

  • Drug-food interactions (DFI) pose risks to drug efficacy and safety.
  • DFI detection is challenging due to the lack of organized databases.
  • Existing resources do not adequately support automated DFI extraction.

Purpose of the Study:

  • To construct a comprehensive database for Drug-Food Interactions (DFI).
  • To develop a natural language processing system for extracting DFI information from biomedical literature.
  • To introduce a novel annotated corpus, the DFI corpus, for DFI extraction tasks.

Main Methods:

  • Formulation of DFI extraction tasks.
  • Manual annotation of biomedical texts for DFI information.
  • Development and application of BERT models pre-trained on biomedical data.

Main Results:

  • Creation of the DFI corpus, comprising 2270 PubMed abstracts and 2498 sentences with DFI/DDI information.
  • The corpus includes detailed information on drug/food entities, evidence levels, and named entity relations.
  • BERT models achieved a 55.0% F1 score in extracting DFI key-sentences.

Conclusions:

  • The DFI corpus is the largest publicly available resource for drug-food interaction research.
  • This corpus enables the development of advanced NLP systems for DFI detection.
  • Facilitates improved understanding and management of DFI risks.