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

Natural Language Processing (706)

Published on in Vol 7, No 4 (2019): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14850, first published .
Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study

Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study

Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study

Authors of this article:

Min Jiang1 Author Orcid Image ;   Todd Sanger1 Author Orcid Image ;   Xiong Liu1 Author Orcid Image
Article Authors Cited by (20) Tweetations (6) Metrics

Journals

  1. Zhang Z, Zhu L, Yu P. Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation. JMIR Medical Informatics 2020;8(5):e17637 View
  2. Jouffroy J, Feldman S, Lerner I, Rance B, Burgun A, Neuraz A. Hybrid Deep Learning for Medication-Related Information Extraction From Clinical Texts in French: MedExt Algorithm Development Study. JMIR Medical Informatics 2021;9(3):e17934 View
  3. Catelli R, Casola V, De Pietro G, Fujita H, Esposito M. Combining contextualized word representation and sub-document level analysis through Bi-LSTM+CRF architecture for clinical de-identification. Knowledge-Based Systems 2021;213:106649 View
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  5. Ramos-Vargas R, Román-Godínez I, Torres-Ramos S. Comparing general and specialized word embeddings for biomedical named entity recognition. PeerJ Computer Science 2021;7:e384 View
  6. Ispirova G, Eftimov T, Koroušić Seljak B. Domain Heuristic Fusion of Multi-Word Embeddings for Nutrient Value Prediction. Mathematics 2021;9(16):1941 View
  7. Xie J, Liu M, Zhou L. CRISPR-OTE: Prediction of CRISPR On-Target Efficiency Based on Multi-Dimensional Feature Fusion. IRBM 2023;44(1):100732 View
  8. Pandey B, Kumar Pandey D, Pratap Mishra B, Rhmann W. A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions. Journal of King Saud University - Computer and Information Sciences 2022;34(8):5083 View
  9. Landolsi M, Romdhane L, Hlaoua L. Medical Named Entity Recognition using Surrounding Sequences Matching. Procedia Computer Science 2022;207:674 View
  10. Chang A, Xian X, Liu M, Zhao X. Health Communication through Positive and Solidarity Messages Amid the COVID-19 Pandemic: Automated Content Analysis of Facebook Uses. International Journal of Environmental Research and Public Health 2022;19(10):6159 View
  11. Fang A, Hu J, Zhao W, Feng M, Fu J, Feng S, Lou P, Ren H, Chen X. Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records. BMC Medical Informatics and Decision Making 2022;22(1) View
  12. Yang L, Huang X, Wang J, Yang X, Ding L, Li Z, Li J. Identifying stroke-related quantified evidence from electronic health records in real-world studies. Artificial Intelligence in Medicine 2023;140:102552 View
  13. Porkoláb Á, Fekete T. A mesterséges intelligencia alkalmazása a nyelvtanulásban. Iskolakultúra 2023;33(8):67 View
  14. Cai L, Li J, Lv H, Liu W, Niu H, Wang Z. Integrating domain knowledge for biomedical text analysis into deep learning: A survey. Journal of Biomedical Informatics 2023;143:104418 View
  15. Li D, Ma H, Li W, Zhao B, Zhao J, Liu Y, Fu J. KTI-RNN: Recognition of Heart Failure from Clinical Notes. Tsinghua Science and Technology 2023;28(1):117 View
  16. Dash A, Darshana S, Yadav D, Gupta V. A clinical named entity recognition model using pretrained word embedding and deep neural networks. Decision Analytics Journal 2024;10:100426 View