A multi-instance multi-label learning approach to objective auscultation analysis of traditional Chinese medicine

Yan, Jianjun; Shen, Qing; Ren, Jintao; Wang, Yiqin; Chen, Chunfeng; Guo, Rui; Yan, Hong‐Bin

doi:10.1109/bmei.2011.6098544

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Moreover, auscultation, classical machine-learning methods like KNN, SVM, Neural network and so on are utilized in auscultation gradually to extract features from speech samples and judge the functions of internal organs and the deficiency or sufficiency of the qi, blood, and body fluids (Yan, 2011). However, AI-assisted interrogation still concentrates on the connection between symptoms and syndromes, which is in the initial stage and more exploration in needed in the future (Wang et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

The Structural Strategies of the International Branding in Traditional Chinese Medicine (TCM) in the Era of Technology Upgrading

Wu¹

2022

Proceedings of the 6th International Conference on Business, Management and Economics

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With a growing desire for holistic and affordable healthcare and treatments, it is possible for Traditional Chinese Medicine (TCM) to offer new perspectives that can benefit modern medicine beyond East Asia. In order to make more people aware of the merits of TCM, this research encourages TCM knowledge and concentrates on branding. The significant focuses of prospect, prescription, product, patent, price, professionalism, partnership, place, people, and philosophy (indicated as the 10 Ps) were selected as the structural strategies of the international branding in TCM to develop the brand equity in the era of digitalization. This study collected data from fiscal reports, government websites and product advertisements related to around 70 TCM production companies in East Asia mainly from China. Several variables are chosen to embody different aspects in 10P, and cluster analysis is employed to figure out the branding strategy pattern among TCM industries under 10P. Because there are both nominal variables and continuous variables, K-Prototype was applied. It is a clustering method based on partitioning, which improve the K-Means and K-Mode clustering algorithm to cluster the mixed data types. New technologies such as virtual reality and novel automotive electronics will continue to emerge and accelerate cross-integration with artificial intelligence (AI) to promote intelligent alternation in production and lifestyle. Combining AI techniques with 10P models, the popularity efficiency of TCM will be improved obviously.

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Section: Discussionmentioning

confidence: 99%

The Structural Strategies of the International Branding in Traditional Chinese Medicine (TCM) in the Era of Technology Upgrading

Wu¹

2022

Proceedings of the 6th International Conference on Business, Management and Economics

View full text Add to dashboard Cite

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“…Experimental results with logistic regression showed the classification rate was better than their previous work. Yan et al [ 50 ] found that most existing approaches were limited to analyze a single vowel, so they introduced multi-instance multilabel learning framework in order to make a comprehensive analysis with five vowels. The experiment results reveal that this method is effective in identifying the health, Qi-deficiency, and Yin-deficiency auscultation data of TCM.…”

Section: Machine Learning Approaches For Tcm Patient Classificatiomentioning

confidence: 99%

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Zhao

Wang

et al. 2015

Evidence-Based Complementary and Alternative Medicine

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As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

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The Impact of Artificial Intelligence on Traditional Chinese Medicine

Wang

Shi

et al. 2021

Am. J. Chin. Med.

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Traditional Chinese Medicine (TCM) is a well-established medical system with a long history. Currently, artificial intelligence (AI) is rapidly expanding in many fields including TCM. AI will significantly improve the reliability and accuracy of diagnostics, thus increasing the use of effective therapeutic methods for patients. This systematic review provides an updated overview on the major breakthroughs in the field of AI-assisted TCM four diagnostic methods, syndrome differentiation, and treatment. AI-assisted TCM diagnosis is mainly based on digital data collected by modern electronic instruments, which makes TCM diagnosis more quantitative, objective, and standardized. As a result, the diagnosis decisions made by different TCM doctors exhibit more consistency, accuracy, and reliability. Meanwhile, the therapeutic efficacy of TCM can be evaluated objectively. Therefore, AI is promoting TCM from experience to evidence-based medicine, a genuine scientific revolution. Furthermore, huge and non-uniform knowledge on formula-syndrome relationships and the combination rules of herbal TCM formulae could be better standardized with the help of AI analysis, which is necessary for the clinical efficacy evaluation and further optimization on the standardized TCM formulae. AI bridges the gap between TCM and modern science and technology. AI may bring clinical TCM diagnostics closer to western medicine. With the help of AI, more scientific evidence about TCM will be discovered. It can be expected that more unified guidelines for specific TCM syndromes will be issued with the development of AI-assisted TCM therapies in the future.

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A multi-instance multi-label learning approach to objective auscultation analysis of traditional Chinese medicine

Cited by 3 publications

References 9 publications

The Structural Strategies of the International Branding in Traditional Chinese Medicine (TCM) in the Era of Technology Upgrading

The Structural Strategies of the International Branding in Traditional Chinese Medicine (TCM) in the Era of Technology Upgrading

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

The Impact of Artificial Intelligence on Traditional Chinese Medicine

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