ArticleDatabase : the journal of biological databases and curation2019
A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning.
Article in Database : the journal of biological databases and curation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 48 citations in OpenAlex.
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- Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction.Quantitative imaging in medicine and surgery · 2023Article
- Could an artificial intelligence approach to prior authorization be more human?Journal of the American Medical Informatics Association : JAMIA · 2023Article
- CoVEffect: interactive system for mining the effects of SARS-CoV-2 mutations and variants based on deep learning.GigaScience · 2022Article
- Identification of Chemical-Disease Associations Through Integration of Molecular Fingerprint, Gene Ontology and Pathway Information.Interdisciplinary sciences, computational life sciences · 2022Article
- An annotated dataset for extracting gene-melanoma relations from scientific literature.Journal of biomedical semantics · 2022Article
- Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021.Evidence-based complementary and alternative medicine : eCAM · 2022Review
- Identification of asthma control factor in clinical notes using a hybrid deep learning model.BMC medical informatics and decision making · 2021Article
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3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
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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