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

Theme issue 2019: Health Natural Language Processing (17) Clinical Information and Decision Making (1434) Complementary and Alternative Medicine (35) Machine Learning (1501) Natural Language Processing (706)

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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17608, first published .
Artificial Intelligence–Based Traditional Chinese Medicine Assistive Diagnostic System: Validation Study

Artificial Intelligence–Based Traditional Chinese Medicine Assistive Diagnostic System: Validation Study

Artificial Intelligence–Based Traditional Chinese Medicine Assistive Diagnostic System: Validation Study

Authors of this article:

Hong Zhang1 Author Orcid Image ;   Wandong Ni2 Author Orcid Image ;   Jing Li1 Author Orcid Image ;   Jiajun Zhang3 Author Orcid Image
Article Authors Cited by (47) Tweetations (2) Metrics

Journals

  1. Shin D, Lee K, Adeluwa T, Hur J. Machine Learning-Based Predictive Modeling of Postpartum Depression. Journal of Clinical Medicine 2020;9(9):2899 View
  2. Zhao Z, Wu C, Zhang S, He F, Liu F, Wang B, Huang Y, Shi W, Jian D, Xie H, Yeh C, Li J. A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study. JMIR Medical Informatics 2021;9(3):e23415 View
  3. Feng C, Shao Y, Wang B, Qu Y, Wang Q, Li Y, Yang T, Jiang L. Development and Application of Artificial Intelligence in Auxiliary TCM Diagnosis. Evidence-Based Complementary and Alternative Medicine 2021;2021:1 View
  4. Yeuk-Lan Alice L, Binghe G, Shuang C, Hoyin C, Kawai K, Wenjung L, Jiangang S. Artificial intelligence meets traditional Chinese medicine: a bridge to opening the magic box of sphygmopalpation for pulse pattern recognition. Digital Chinese Medicine 2021;4(1):1 View
  5. 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
  6. Wang Y, Shi X, Li L, Efferth T, Shang D. The Impact of Artificial Intelligence on Traditional Chinese Medicine. The American Journal of Chinese Medicine 2021;49(06):1297 View
  7. Wang J, Duan L, Li H, Liu J, Chen H. Construction of an Artificial Intelligence Traditional Chinese Medicine Diagnosis and Treatment Model Based on Syndrome Elements and Small-Sample Data. Engineering 2022;8:29 View
  8. 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
  9. Ruan Q, Wu Q, Yao J, Wang Y, Tseng H, Zhang Z. An Efficient Tongue Segmentation Model Based on U-Net Framework. International Journal of Pattern Recognition and Artificial Intelligence 2021;35(16) View
  10. He Y, Wen Q, Wang Y, Li J, Li N, Jin R, Li N, Zhang Y, Zhang L. Establishing a Regulatory Science System for Supervising the Application of Artificial Intelligence for Traditional Chinese Medicine: A Methodological Framework. Evidence-Based Complementary and Alternative Medicine 2022;2022:1 View
  11. Zhang H, Zhang J, Ni W, Jiang Y, Liu K, Sun D, Li J. Transformer- and Generative Adversarial Network–Based Inpatient Traditional Chinese Medicine Prescription Recommendation: Development Study. JMIR Medical Informatics 2022;10(5):e35239 View
  12. 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
  13. 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
  14. Xia Y, Cai J, Li Y, Dou Z, Zhang Y, Wu L, Huang Z, Xu S, Sun J, Liu Y, Wu D, Han D. A precision‐preferred comprehensive information extraction system for clinical articles in traditional Chinese Medicine. International Journal of Intelligent Systems 2022;37(8):4994 View
  15. Zhou L, Liu S, Li C, Sun Y, Zhang Y, Li Y, Yuan H, Sun Y, Xu F, Li Y, Ahmed M. Natural Language Processing Algorithms for Normalizing Expressions of Synonymous Symptoms in Traditional Chinese Medicine. Evidence-Based Complementary and Alternative Medicine 2021;2021:1 View
  16. Zhang T, Huang Z, Wang Y, Wen C, Peng Y, Ye Y, Zhou X. Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021. Evidence-Based Complementary and Alternative Medicine 2022;2022:1 View
  17. 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
  18. Chu H, Moon S, Park J, Bak S, Ko Y, Youn B. The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review. Frontiers in Pharmacology 2022;13 View
  19. Ren G, Yu K, Xie Z, Liu L, Wang P, Zhang W, Wang Y, Wu X. Differentiation of lumbar disc herniation and lumbar spinal stenosis using natural language processing–based machine learning based on positive symptoms. Neurosurgical Focus 2022;52(4):E7 View
  20. Li N, Yu J, Mao X, Zhao Y, Huang L, Gu J. The Research and Development Thinking on the Status of Artificial Intelligence in Traditional Chinese Medicine. Evidence-Based Complementary and Alternative Medicine 2022;2022:1 View
  21. Gong L. Ml-Prdf：A Syndrome Differentiation Model of Traditional Chinese Medicine Based on Pcc-Mlrf and Multi-Label Deep Forest. SSRN Electronic Journal 2022 View
  22. Ma S, Liu J, Li W, Liu Y, Hui X, Qu P, Jiang Z, Li J, Wang J. Machine learning in TCM with natural products and molecules: current status and future perspectives. Chinese Medicine 2023;18(1) View
  23. Xi C, Zhang J, Xiao Y, Liu J, Liu W, Tian D, Liu Y, Zhai S, Ye H. Research on Application Paradigm of Random Sampling Method in Data Analysis of Traditional Chinese Medicine—Taking Insomnia Prescriptions of Famous Old Tcm Physicians as an Example. SSRN Electronic Journal 2022 View
  24. Meng H, Han Y, Zan Z. Application of multi-sensor network and artificial intelligence in health monitoring of medical geriatric care. Soft Computing 2023 View
  25. Yang X, Ding C. SMRGAT: A traditional Chinese herb recommendation model based on a multi-graph residual attention network and semantic knowledge fusion. Journal of Ethnopharmacology 2023;315:116693 View
  26. Gong L, Jiang J, Chen S, Qi M. A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining. Frontiers in Genetics 2023;14 View
  27. Sim J, Huang X, Horan M, Stewart C, Robison L, Hudson M, Baker J, Huang I. Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review. Artificial Intelligence in Medicine 2023;146:102701 View
  28. Zhou X, Yang Q, Bi L, Wang S. Integrating traditional apprenticeship and modern educational approaches in traditional Chinese medicine education. Medical Teacher 2024;46(6):792 View
  29. 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
  30. Li W, Ge X, Liu S, Xu L, Zhai X, Yu L. Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence. Frontiers in Medicine 2024;10 View
  31. 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
  32. Ng J, Cramer H, Lee M, Moher D. Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare. Integrative Medicine Research 2024;13(1):101024 View
  33. Ke Z, Liu M, Liu J, Su Z, Li L, Qian M, Zhang X, Cao L, Wang T, Wang Z, Xiao W. The Application of Artificial Intelligence in the Research and Development of Traditional Chinese Medicine. International Journal of Drug Discovery and Pharmacology 2024:100001 View
  34. 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
  35. KONG Q, CHEN L, YAO J, DING C, YIN P. Feasibility and Challenges of Interactive AI for Traditional Chinese Medicine: An Example of ChatGPT. Chinese Medicine and Culture 2024 View
  36. Madanay F, Tu K, Campagna A, Davis J, Doerstling S, Chen F, Ubel P. Classification of Patients’ Judgments of Their Physicians in Web-Based Written Reviews Using Natural Language Processing: Algorithm Development and Validation. Journal of Medical Internet Research 2024;26:e50236 View
  37. Song Z, Chen G, Chen C. AI empowering traditional Chinese medicine?. Chemical Science 2024;15(41):16844 View
  38. Li L, Guan Y, Du Y, Chen Z, Xie H, Lu K, Kang J, Jin P. Exploiting omic-based approaches to decipher Traditional Chinese Medicine. Journal of Ethnopharmacology 2025;337:118936 View
  39. Lim J, Li J, Zhou M, Xiao X, Xu Z. Machine Learning Research Trends in Traditional Chinese Medicine: A Bibliometric Review. International Journal of General Medicine 2024;Volume 17:5397 View
  40. Hong Y, Zhu S, Liu Y, Tian C, Xu H, Chen G, Tao L, Xie T. The integration of machine learning into traditional Chinese medicine. Journal of Pharmaceutical Analysis 2024:101157 View
  41. Yoon D, Moon H, Lee I, Chae Y. Discovering the key symptoms for identifying patterns in functional dyspepsia patients: doctor's decision and machine learning. Integrative Medicine Research 2024:101115 View

Books/Policy Documents

  1. Ren M, Huang H, Zhou Y, Cao Q, Bu Y, Gao Y. Chinese Computational Linguistics. View
  2. Karpa D, Klarl T, Rochlitz M. Diginomics Research Perspectives. View
  3. Zhang Z, Wang Y, Liu S, Wang L, Zhang M. Cognitive Systems and Information Processing. View
  4. Huang H, Mo Y. AI Methods and Applications in 3D Technologies. View