A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning Article Swipe
Tao Chen
,
Mingfen Wu
,
Hexi Li
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1093/database/baz116
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1093/database/baz116
The automatic extraction of meaningful relations from biomedical literature or clinical records is crucial in various biomedical applications. Most of the current deep learning approaches for medical relation extraction require large-scale training data to prevent overfitting of the training model. We propose using a pre-trained model and a fine-tuning technique to improve these approaches without additional time-consuming human labeling. Firstly, we show the architecture of Bidirectional Encoder Representations from Transformers (BERT), an approach for pre-training a model on large-scale unstructured text. We then combine BERT with a one-dimensional convolutional neural network (1d-CNN) to fine-tune the pre-trained model for relation extraction. Extensive experiments on three datasets, namely the BioCreative V chemical disease relation corpus, traditional Chinese medicine literature corpus and i2b2 2012 temporal relation challenge corpus, show that the proposed approach achieves state-of-the-art results (giving a relative improvement of 22.2, 7.77, and 38.5% in F1 score, respectively, compared with a traditional 1d-CNN classifier). The source code is available at https://github.com/chentao1999/MedicalRelationExtraction.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/database/baz116
- https://academic.oup.com/database/article-pdf/doi/10.1093/database/baz116/31197246/baz116.pdf
- OA Status
- gold
- Cited By
- 46
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2997204042
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2997204042Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/database/baz116Digital Object Identifier
- Title
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A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-01-01Full publication date if available
- Authors
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Tao Chen, Mingfen Wu, Hexi LiList of authors in order
- Landing page
-
https://doi.org/10.1093/database/baz116Publisher landing page
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https://academic.oup.com/database/article-pdf/doi/10.1093/database/baz116/31197246/baz116.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/database/article-pdf/doi/10.1093/database/baz116/31197246/baz116.pdfDirect OA link when available
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Computer science, Relation (database), Relationship extraction, Artificial intelligence, Deep learning, Extraction (chemistry), Machine learning, Natural language processing, Information retrieval, Data mining, Chromatography, ChemistryTop concepts (fields/topics) attached by OpenAlex
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46Total citation count in OpenAlex
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2025: 3, 2024: 13, 2023: 11, 2022: 8, 2021: 9Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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