Published on in Vol 10, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29290, first published .
A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation

A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation

A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation

Journals

  1. Niu Q, Li H, Tong L, Liu S, Zong W, Zhang S, Tian S, Wang J, Liu J, Li B, Wang Z, Zhang H. TCMFP: a novel herbal formula prediction method based on network target’s score integrated with semi-supervised learning genetic algorithms. Briefings in Bioinformatics 2023;24(3) View
  2. Ma Y, Zhong Y, Su Q, Xu L, Song H, Wen C. Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network. Medicine 2023;102(25):e34085 View
  3. Shi W, Zhang J, Yu B, Li Y, Cheng S. A Multi-Objective Hyper-Heuristic Clustering Algorithm for Formulas in Traditional Chinese Medicine. IEEE Access 2023;11:100355 View
  4. Li X, Chen K, Yang J, Wang C, Yang T, Luo C, Li N, Liu Z. TLDA: A transfer learning based dual-augmentation strategy for traditional Chinese Medicine syndrome differentiation in rare disease. Computers in Biology and Medicine 2024;169:107808 View
  5. Hu H, Cheng C, Ye Q, Peng L, Shen Y. Enhancing traditional Chinese medicine diagnostics: Integrating ontological knowledge for multi-label symptom entity classification. Mathematical Biosciences and Engineering 2023;21(1):369 View
  6. Chen Z, Zhang D, Liu C, Wang H, Jin X, Yang F, Zhang J. Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning. Integrative Medicine Research 2024;13(1):101019 View
  7. Li Z, Zhao H, Zhu G, Du J, Wu Z, Jiang Z, Li Y. Classification method of traditional Chinese medicine compound decoction duration based on multi-dimensional feature weighted fusion. Computer Methods in Biomechanics and Biomedical Engineering 2024:1 View
  8. Tian D, Chen W, Xu D, Xu L, Xu G, Guo Y, Yao Y. A review of traditional Chinese medicine diagnosis using machine learning: Inspection, auscultation-olfaction, inquiry, and palpation. Computers in Biology and Medicine 2024;170:108074 View
  9. Pan D, Guo Y, Fan Y, Wan H. Development and Application of Traditional Chinese Medicine Using AI Machine Learning and Deep Learning Strategies. The American Journal of Chinese Medicine 2024;52(03):605 View
  10. Zeng H, Xu J, Zheng L, Zhan Z, Fang Z, Li Y, Zhao C, Xiao R, Zheng Z, Li Y, Yang L. Traditional Chinese herbal formulas modulate gut microbiome and improve insomnia in patients with distinct syndrome types: insights from an interventional clinical study. Frontiers in Cellular and Infection Microbiology 2024;14 View
  11. Lu L, Ni S, He X, Huang Y, Chen X, Yang Z. From Tradition to Evidence-base: Leveraging TCM Human Use Experience in Modern Drug Development. SSRN Electronic Journal 2024 View
  12. Lee Y, Chae Y. Pattern Identification and Acupuncture Prescriptions Based on Real-World Data Using Artificial Intelligence. East Asian Science, Technology and Society: An International Journal 2024:1 View
  13. Lu L, Lu T, Tian C, Zhang X. AI: Bridging Ancient Wisdom and Modern Innovation in Traditional Chinese Medicine. JMIR Medical Informatics 2024;12:e58491 View
  14. Lim J, Li J, Feng X, Feng L, Xiao X, Zhou M, Yang H, Xu Z. Predicting TCM patterns in PCOS patients: An exploration of feature selection methods and multi-label machine learning models. Heliyon 2024;10(15):e35283 View
  15. Lu L, Ni S, He X, Huang Y, Chen X, Yang Z. From tradition to evidence-base: Leveraging TCM human use experience in modern drug development. Pharmacological Research - Modern Chinese Medicine 2024;13:100535 View

Books/Policy Documents

  1. Huang H, Mo Y. AI Methods and Applications in 3D Technologies. View