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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.
Motivation:
While drug-food interaction (DFI) may undermine the efficacy and safety of drugs, DFI detection has been difficult because a well-organized database for DFI did not exist. To construct a DFI database and build a natural language processing system extracting DFI from biomedical articles, we formulated the DFI extraction tasks and manually annotated texts that could have contained DFI information. In this article, we introduced a new annotated corpus for extracting DFI, the DFI corpus.
Results:
The DFI corpus contains 2270 abstracts of biomedical articles accessible through PubMed and 2498 sentences that contain DFI and/or drug-drug information (DDI), a substantial amount of information about drug/food entities, evidence-levels of abstracts and relations between named entities. BERT models pre-trained on the biomedical domain achieved a F1 score 55.0% in extracting DFI key-sentences. To the best of our knowledge, the DFI corpus is the largest public corpus for drug-food interaction.
Availability And Implementation:
Our corpus is available at https://github.com/ccadd-snu/corpus-for-DFI-extraction.
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