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Cited by 24Biomedical Text Mining and OntologiesTopic ModelingAdvanced Text Analysis Techniques

Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation

Open access
Wang, Jian, Chen, Xiaoyu, Zhang, Yu, Zhang, Yijia, Wen, Jiabin, Lin, Hongfei, Yang, Zhihao, Wang, Xin·2020·JMIR Medical Informatics
DOI 10.2196/17638PMC PMC7458061

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Abstract

Background: Automatically extracting relations between chemicals and diseases plays an important role in biomedical text mining. Chemical-disease relation (CDR) extraction aims at extracting complex semantic relationships between entities in documents, which contain intrasentence and intersentence relations. Most previous methods did not consider dependency syntactic information across the sentences, which are very valuable for the relations extraction task, in particular, for extracting the intersentence relations accurately. Objective: In this paper, we propose a novel end-to-end neural network based on the graph convolutional network (GCN) and multihead attention, which makes use of the dependency syntactic information across the sentences to improve CDR extraction task. Methods: To improve the performance of intersentence relation extraction, we constructed a document-level dependency graph to capture the dependency syntactic information across sentences. GCN is applied to capture the feature representation of the document-level dependency graph. The multihead attention mechanism is employed to learn the relatively important context features from different semantic subspaces. To enhance the input representation, the deep context representation is used in our model instead of traditional word embedding. Results: We evaluate our method on CDR corpus. The experimental results show that our method achieves an F-measure of 63.5%, which is superior to other state-of-the-art methods. In the intrasentence level, our method achieves a precision, recall, and F-measure of 59.1%, 81.5%, and 68.5%, respectively. In the intersentence level, our method achieves a precision, recall, and F-measure of 47.8%, 52.2%, and 49.9%, respectively. Conclusions: The GCN model can effectively exploit the across sentence dependency information to improve the performance of intersentence CDR extraction. Both the deep context representation and multihead attention are helpful in the CDR extraction task.

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  • Machine learning methods, particularly deep learning, typically employ neural networks and first-generation transformer-based large language models (e.g., BERT).

    “Machine learning methods11–14, particularly deep learning, typically employ neural networks and the first generation of transformer-based large language models (e.g., BERT).”

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Research works printed but not linked: 5

  1. [8]Zhou H Deng H He J Chemical-disease relations extraction based on the shortest dependency path tree 2015 Proceedings of the Fifth BioCreative Challenge Evaluation Workshop 2015 Seville, Spain 214 219
  2. [16]Vaswani A Shazeer N Parmar N Attention is all you need 2017 Neural Information Processing Systems(NIPS) 2017 Long Beach, USA 5998 6008
  3. [25]Bahdanau D Cho K Bengio Y Neural Machine Translation by Jointly Learning to Align and Translate 2015 3rd International Conference on Learning Representations May 7-9, 2015 San Diego, CA, USA
  4. [28]Santoro A Raposo D Barrett D Malinowski M Pascanu R Battaglia P Lillicrap T A simple neural network module for relational reasoning 2017 Advances in Neural Information Processing Systems 2017 Long Beach, USA 4967 4976
  5. [30]Kingma DP Ba J Adam:A Method for Stochastic Optimization 2015 the 3rd International Conference for Learning Representations 2015 San Diego

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Authors
Wang, J., Chen, X., Zhang, Y., Zhang, Y., Wen, J., Lin, H., Yang, Z., & Wang, X.
Affiliations
  • Dalian University of Technology (CN)
  • Dalian Medical University (CN)
Corresponding author
Zhang, Yijia
ORCID
Venue
JMIR Medical Informatics
Publisher
JMIR Publications Inc.
Published
2020-07-31
Volume
8
Issue
7
Pages
e17638
ISSN
2291-9694 (ISSN-L)
DOI
10.2196/17638
PMC
PMC7458061
OpenAlex
W3017538732
Cited by
24
References
27
Citations by year
2026: 2 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 5 · 2021: 5 · 2020: 1
FWCI
1.22
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Medical Informatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Natural Science Foundation of China (61572102)., 61572098)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
Keywords
document-level relation extraction, graph convolutional networks, multi-head attention, biomedical text mining

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