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

Machine Learning (1465) Ontologies, Classifications, and Coding (239) Memory (43)

Published on in Vol 8, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19848, first published .
Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations

Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations

Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations

Authors of this article:

Yongbin Li1 Author Orcid Image ;   Xiaohua Wang1 Author Orcid Image ;   Linhu Hui1 Author Orcid Image ;   Liping Zou1 Author Orcid Image ;   Hongjin Li1 Author Orcid Image ;   Luo Xu1 Author Orcid Image ;   Weihai Liu2 Author Orcid Image
Article Authors Cited by (26) Tweetations (2) Metrics

Journals

  1. Kong J, Zhang L, Jiang M, Liu T. Incorporating multi-level CNN and attention mechanism for Chinese clinical named entity recognition. Journal of Biomedical Informatics 2021;116:103737 View
  2. Lee H, Kang J, Yeo J. Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment. Journal of Medical Internet Research 2021;23(5):e27460 View
  3. Ispirova G, Eftimov T, Koroušić Seljak B. Domain Heuristic Fusion of Multi-Word Embeddings for Nutrient Value Prediction. Mathematics 2021;9(16):1941 View
  4. Wang C, Gao J, Rao H, Chen A, He J, Jiao J, Zou N, Gu L. Named entity recognition (NER) for Chinese agricultural diseases and pests based on discourse topic and attention mechanism. Evolutionary Intelligence 2024;17(1):457 View
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  7. He L, Zhang X, Li Z, Xiao P, Wei Z, Cheng X, Qu S, Yuan M. A Chinese Named Entity Recognition Model of Maintenance Records for Power Primary Equipment Based on Progressive Multitype Feature Fusion. Complexity 2022;2022(1) View
  8. Zheng Y, Han Z, Cai Y, Duan X, Sun J, Yang W, Huang H. An imConvNet-based deep learning model for Chinese medical named entity recognition. BMC Medical Informatics and Decision Making 2022;22(1) View
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  10. Qi R, Lv P, Zhang Q, Wu M. Research on Chinese Medical Entity Recognition Based on Multi-Neural Network Fusion and Improved Tri-Training Algorithm. Applied Sciences 2022;12(17):8539 View
  11. Guo W, Lu J, Han F. Named Entity Recognition for Chinese Electronic Medical Records Based on Multitask and Transfer Learning. IEEE Access 2022;10:77375 View
  12. Yamanouchi Y, Nakamura T, Ikeda T, Usuku K. An Alternative Application of Natural Language Processing to Express a Characteristic Feature of Diseases in Japanese Medical Records. Methods of Information in Medicine 2023;62(03/04):110 View
  13. Wang W, Li X, Ren H, Gao D, Fang A. Chinese Clinical Named Entity Recognition From Electronic Medical Records Based on Multisemantic Features by Using Robustly Optimized Bidirectional Encoder Representation From Transformers Pretraining Approach Whole Word Masking and Convolutional Neural Networks: Model Development and Validation. JMIR Medical Informatics 2023;11:e44597 View
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  17. Zhi Y, Tao X, Ji Y. MFF-CNER: A Multi-feature Fusion Model for Chinese Named Entity Recognition in Finance Securities. Academic Journal of Science and Technology 2023;7(3):40 View
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  19. Du H, Xu J, Du Z, Chen L, Ma S, Wei D, Wang X. MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records. Interdisciplinary Sciences: Computational Life Sciences 2024;16(2):489 View
  20. Lu Y, Zhao R, Wen X, Tong X, Xiang D, Zhang J. Medical Named Entity Recognition Model Based on Knowledge Graph Enhancement. International Journal of Pattern Recognition and Artificial Intelligence 2024;38(04) View

Books/Policy Documents

  1. Du X, Jia Y, Zan H. Chinese Computational Linguistics. View