This paper is in the following e-collection/theme issue:

Theme issue 2019: Health Natural Language Processing (17) Machine Learning (1465) Natural Language Processing (693)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17638, first published .
Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation

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

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

Authors of this article:

Jian Wang1 Author Orcid Image ;   Xiaoyu Chen1 Author Orcid Image ;   Yu Zhang1 Author Orcid Image ;   Yijia Zhang1 Author Orcid Image ;   Jiabin Wen2 Author Orcid Image ;   Hongfei Lin1 Author Orcid Image ;   Zhihao Yang1 Author Orcid Image ;   Xin Wang1 Author Orcid Image
Article Authors Cited by (16) Tweetations (1) Metrics

Journals

  1. Zeng D, Zhao C, Quan Z. CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction. Frontiers in Genetics 2021;12 View
  2. Guo S, Huang L, Yao G, Wang Y, Guan H, Bai T. Extracting Biomedical Entity Relations using Biological Interaction Knowledge. Interdisciplinary Sciences: Computational Life Sciences 2021;13(2):312 View
  3. Hao T, Huang Z, Liang L, Weng H, Tang B. Health Natural Language Processing: Methodology Development and Applications. JMIR Medical Informatics 2021;9(10):e23898 View
  4. Zhu X, Zhang L, Du J, Xiao Z. Full-Abstract Biomedical Relation Extraction with Keyword-Attentive Domain Knowledge Infusion. Applied Sciences 2021;11(16):7318 View
  5. Li Z, Chen H, Qi R, Lin H, Chen H. DocR-BERT: Document-Level R-BERT for Chemical-Induced Disease Relation Extraction via Gaussian Probability Distribution. IEEE Journal of Biomedical and Health Informatics 2022;26(3):1341 View
  6. Li Z, Wang M, Peng D, Liu J, Xie Y, Dai Z, Zou X. Identification of Chemical–Disease Associations Through Integration of Molecular Fingerprint, Gene Ontology and Pathway Information. Interdisciplinary Sciences: Computational Life Sciences 2022;14(3):683 View
  7. Qianmin S, Wei P, Xiaoqiong C, Hongxing L, Jihan H. COVID‐19 clinical medical relationship extraction based on MPNet. IET Cyber-Physical Systems: Theory & Applications 2023;8(2):119 View
  8. Yang Y, Lu Y, Yan W. A comprehensive review on knowledge graphs for complex diseases. Briefings in Bioinformatics 2023;24(1) View
  9. Pham Thi Q, Dao Q, Nguyen A, Dang T. Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph. International Journal of Computational Intelligence Systems 2023;16(1) View
  10. Lai P, Wei C, Luo L, Chen Q, Lu Z. BioREx: Improving biomedical relation extraction by leveraging heterogeneous datasets. Journal of Biomedical Informatics 2023;146:104487 View
  11. Zhou B, Yang G, Shi Z, Ma S. Natural Language Processing for Smart Healthcare. IEEE Reviews in Biomedical Engineering 2024;17:4 View
  12. Huang J, Yang D, Rong R, Nezafati K, Treager C, Chi Z, Wang S, Cheng X, Guo Y, Klesse L, Xiao G, Peterson E, Zhan X, Xie Y. A critical assessment of using ChatGPT for extracting structured data from clinical notes. npj Digital Medicine 2024;7(1) View
  13. Gao C, Xu G, Meng Y. Integrated Extraction of Entities and Relations via Attentive Graph Convolutional Networks. Electronics 2024;13(22):4373 View