Digitalizing Traditional Chinese Medicine Pulse Diagnosis with Artificial Neural Network

2012 ◽  
Vol 18 (6) ◽  
pp. 446-453 ◽  
Author(s):  
Anson C.Y. Tang ◽  
Joanne W.Y. Chung ◽  
Thomas K.S. Wong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Pulse Diagnosis ◽  
Artificial Neural

scholarly journals Validation of a Novel Traditional Chinese Medicine Pulse Diagnostic Model Using an Artificial Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Anson Chui Yan Tang ◽  
Joanne Wai Yee Chung ◽  
Thomas Kwok Shing Wong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Essential Hypertension ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Diagnostic Model ◽  
Criterion Validation ◽  
Specificity And Sensitivity ◽  
Group A ◽  
Artificial Neural

In view of lacking a quantifiable traditional Chinese medicine (TCM) pulse diagnostic model, a novel TCM pulse diagnostic model was introduced to quantify the pulse diagnosis. Content validation was performed with a panel of TCM doctors. Criterion validation was tested with essential hypertension. The gold standard was brachial blood pressure measured by a sphygmomanometer. Two hundred and sixty subjects were recruited (139 in the normotensive group and 121 in the hypertensive group). A TCM doctor palpated pulses at left and right cun, guan, and chi points, and quantified pulse qualities according to eight elements (depth, rate, regularity, width, length, smoothness, stiffness, and strength) on a visual analog scale. An artificial neural network was used to develop a pulse diagnostic model differentiating essential hypertension from normotension. Accuracy, specificity, and sensitivity were compared among various diagnostic models. About 80% accuracy was attained among all models. Their specificity and sensitivity varied, ranging from 70% to nearly 90%. It suggested that the novel TCM pulse diagnostic model was valid in terms of its content and diagnostic ability.

A Back Propagation Artificial Neural Network Applied to Body Constitution in Traditional Chinese Medicine Modeling

2013 ◽  
Vol 659 ◽  
pp. 123-127
Author(s):  
Zhi Biao Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Architecture ◽  
Back Propagation ◽  
Good Prediction ◽  
Body Constitution ◽  
Yin Yang ◽  
Artificial Neural

In this paper, artificial neural network architecture is introduced to predict the Yin-Yang index of body constitution in traditional Chinese medicine (BCTCM). With pre-processing the inputting data by the median, the collected data is more consistent with the exact value of the characteristic parameters of BCTCM. Quasi-Newton algorithm is used to train the network model to accelerate the convergence speed of network training. Experiments show that, the result showed that they had good prediction accuracies for BCTTCM. The mean absolute error for 10 true measured points was 0.034. Therefore, the prediction model of BCTCM Yin-Yang index with BP neural network is doable.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document