scholarly journals Machine-readable Yin–Yang imbalance: traditional Chinese medicine syndrome computer modeling based on three-dimensional noninvasive cardiac electrophysiology imaging

2019 ◽  
Vol 47 (4) ◽  
pp. 1580-1591 ◽  
Author(s):  
Wei Cen ◽  
Ralph Hoppe ◽  
Aiwu Sun ◽  
Hongyan Ding ◽  
Ning Gu
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Electrical Activity ◽  
Computer Modeling ◽  
Cardiac Electrophysiology ◽  
Three Dimensional ◽  
Mathematical Physics ◽  
Syndrome Differentiation ◽  
Physics Model ◽  
Machine Readable

Objectives The principal diagnostic methods of traditional Chinese medicine (TCM) are inspection, auscultation and olfaction, inquiry, and pulse-taking. Treatment by syndrome differentiation is likely to be subjective. This study was designed to provide a basic theory for TCM diagnosis and establish an objective means of evaluating the correctness of syndrome differentiation. Methods We herein provide the basic theory of TCM syndrome computer modeling based on a noninvasive cardiac electrophysiology imaging technique. Noninvasive cardiac electrophysiology imaging records the heart’s electrical activity from hundreds of electrodes on the patient’s torso surface and therefore provides much more information than 12-lead electrocardiography. Through mathematical reconstruction algorithm calculations, the reconstructed heart model is a machine-readable description of the underlying mathematical physics model that reveals the detailed three-dimensional (3D) electrophysiological activity of the heart. Results From part of the simulation results, the imaged 3D cardiac electrical source provides dynamic information regarding the heart’s electrical activity at any given location within the 3D myocardium. Conclusions This noninvasive cardiac electrophysiology imaging method is suitable for translating TCM syndromes into a computable format of the underlying mathematical physics model to offer TCM diagnosis evidence-based standards for ensuring correct evaluation and rigorous, scientific data for demonstrating its efficacy.

scholarly journals Treatment of Oral Ulcer Based on TCM Syndrome Differentiation

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Ying Yao ◽  
Li Liu
Keyword(s):  
Quality Of Life ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Western Medicine ◽  
Severe Case ◽  
Oral Ulcer ◽  
Syndrome Differentiation ◽  
Tcm Syndrome ◽  
Chinese Medicine Treatment

Oral ulcer is a kind of ulcerative injury that occurs in the oral mucosa and is very common in clinic. In severe case, it can affect the quality of life of the patients. Western medicine treatment of oral ulcer is often prone to relapse, while the effect of traditional Chinese medicine treatment is remarkable.

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

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Changbo Zhao ◽  
Guo-Zheng Li ◽  
Chengjun Wang ◽  
Jinling Niu
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Machine Learning Techniques ◽  
Syndrome Differentiation ◽  
Reference List ◽  
Patient Classification ◽  
Research Issues

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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