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

Theme issue 2019: Health Natural Language Processing (16) Complementary and Alternative Medicine (18) Natural Language Processing (553) Ontologies, Classifications, and Coding (177) Innovations and Technology in Cancer Care (350) Innovations in Cancer Diagnostic and Decision Support (45) Lung Cancer (24)

Published on in Vol 8, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17821, first published .
End-to-End Models to Imitate Traditional Chinese Medicine Syndrome Differentiation in Lung Cancer Diagnosis: Model Development and Validation

End-to-End Models to Imitate Traditional Chinese Medicine Syndrome Differentiation in Lung Cancer Diagnosis: Model Development and Validation

End-to-End Models to Imitate Traditional Chinese Medicine Syndrome Differentiation in Lung Cancer Diagnosis: Model Development and Validation

Authors of this article:

Ziqing Liu 1, 2 Author Orcid Image ;   Haiyang He 3 Author Orcid Image ;   Shixing Yan 3 Author Orcid Image ;   Yong Wang 4 Author Orcid Image ;   Tao Yang 2 Author Orcid Image ;   Guo-Zheng Li 1 Author Orcid Image
Article Authors Cited by (13) Tweetations (1) Metrics

Journals

  1. 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
  2. Zhao Z, Song K, Ren X, Qiang Y, Zhao J, Hou J, Zhu J, Xiao N, Zhang J. Attention Matching Network for few-shot learning in the syndrome differentiation of cerebral stroke. International Journal of Machine Learning and Cybernetics 2023;14(3):911 View
  3. Hussain Ali Y, Sabu Chooralil V, Balasubramanian K, Manyam R, Kidambi Raju S, T. Sadiq A, Farhan A. Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer. Bioengineering 2023;10(3):320 View
  4. Chenais G, Gil-Jardiné C, Touchais H, Avalos Fernandez M, Contrand B, Tellier E, Combes X, Bourdois L, Revel P, Lagarde E. Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory: Development and Validation Study. JMIR AI 2023;2:e40843 View
  5. Lv B, Wu L, Huangfu T, He J, Chen W, Tan L, Mok D. Traditional Chinese Medicine Recognition Based on Target Detection. Evidence-Based Complementary and Alternative Medicine 2022;2022:1 View
  6. El-Shafai W, A. Mahmoud A, M. El-Rabaie E, E. Taha T, F. Zahran O, S. El-Fishawy A, Abd-Elnaby M, E. Abd El-Samie F. Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning. Computers, Materials & Continua 2022;71(1):159 View
  7. Gatto J, Seegmiller P, Johnston G, Preum S. Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning–Based Solution. JMIR Medical Informatics 2022;10(9):e37770 View
  8. Ye Q, Yang R, Cheng C, Peng L, Lan Y, Wen T. Combining the External Medical Knowledge Graph Embedding to Improve the Performance of Syndrome Differentiation Model. Evidence-Based Complementary and Alternative Medicine 2023;2023:1 View
  9. SUI D, ZHANG L, YANG F. Data-driven based four examinations in TCM: a survey. Digital Chinese Medicine 2022;5(4):377 View
  10. Hu C, Zhang S, Gu T, Yan Z, Jiang J. Multi-Task Joint Learning Model for Chinese Word Segmentation and Syndrome Differentiation in Traditional Chinese Medicine. International Journal of Environmental Research and Public Health 2022;19(9):5601 View
  11. Huang Z, Miao J, Chen J, Zhong Y, Yang S, Ma Y, Wen C. A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation. JMIR Medical Informatics 2022;10(4):e29290 View

Books/Policy Documents

  1. Ren M, Huang H, Zhou Y, Cao Q, Bu Y, Gao Y. Chinese Computational Linguistics. View