Deep learning in clinical natural language processing: a methodical review
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2019/12/03
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Indian UGC (journal)
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Refrences117
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Citations186
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Stephen Wu School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Kirk Roberts School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA ORCID (unauthenticated)
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Surabhi Datta School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Jingcheng Du School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Zongcheng Ji School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Yuqi Si School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Sarvesh Soni School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Qiong Wang School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Qiang Wei School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA ORCID (unauthenticated)
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Yang Xiang School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Bo Zhao School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Hua Xu School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
Abstract
Cite
Wu, Stephen, et al. “Deep Learning in Clinical Natural Language Processing: A Methodical Review”. Journal of the American Medical Informatics Association, vol. 27, no. 3, 2019, pp. 457-70, https://doi.org/10.1093/jamia/ocz200.
Wu, S., Roberts, K., Datta, S., Du, J., Ji, Z., Si, Y., Soni, S., Wang, Q., Wei, Q., Xiang, Y., Zhao, B., & Xu, H. (2019). Deep learning in clinical natural language processing: a methodical review. Journal of the American Medical Informatics Association, 27(3), 457-470. https://doi.org/10.1093/jamia/ocz200
Wu, Stephen, Kirk Roberts, Surabhi Datta, Jingcheng Du, Zongcheng Ji, Yuqi Si, Sarvesh Soni, et al. “Deep Learning in Clinical Natural Language Processing: A Methodical Review”. Journal of the American Medical Informatics Association 27, no. 3 (2019): 457-70. https://doi.org/10.1093/jamia/ocz200.
Wu S, Roberts K, Datta S, Du J, Ji Z, Si Y, et al. Deep learning in clinical natural language processing: a methodical review. Journal of the American Medical Informatics Association. 2019;27(3):457-70.
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Medicine
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Refrences
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|---|---|---|---|---|
| Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding | Drug Safety |
| 34 | 2019 |
| Adverse Drug Events Detection in Clinical Notes by Jointly Modeling Entities and Relations Using Neural Networks | Drug Safety |
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| A guide to deep learning in healthcare | Nature Medicine |
| 1,381 | 2019 |
| A hybrid approach for named entity recognition in Chinese electronic medical record | BMC Medical Informatics and Decision Making |
| 30 | 2019 |
| Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text | BMC Medical Informatics and Decision Making |
| 22 | 2019 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
GPT for medical entity recognition in Spanish | Multimedia Tools and Applications |
| 2024 | |
| A Defect Data Compensation Model for Infrared Thermal Imaging Based on Bi-LSTM with Attention Mechanism | JOM |
| 2024 | |
| Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model | Artificial Intelligence in Medicine |
| 2024 | |
| Development and validation of a semi-supervised deep learning model for automatic retinopathy of prematurity staging | iScience | 2024 | ||
| Obtaining clinical term embeddings from SNOMED CT ontology | Journal of Biomedical Informatics |
| 2024 |
Citations Analysis
The category
Medicine: Medicine (General): Computer applications to medicine. Medical informatics 60
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Annotation and extraction of age and temporally-related events from clinical histories and was published in 2020. The most recent citation comes from a 2024 study titled Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach. This article reached its peak citation in 2023, with 61 citations. It has been cited in 116 different journals, 32% of which are open access. Among related journals, the Journal of Biomedical Informatics cited this research the most, with 8 citations. The chart below illustrates the annual citation trends for this article.