Guoping Liu

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In traditional Chinese medicine (TCM) diagnosis, a patient may be associated with more than one syndrome tags, and its computer-aided diagnosis is a typical application in the domain of multi-label learning of high-dimensional data. It is common that a great deal of symptoms can occur in traditional Chinese medical diagnosis, which affects the modeling of(More)
This paper introduces a standard acquisition environment and a recognition method for clinical facial complexional diagnosis in Traditional Chinese Medicine (TCM). Firstly, collected the information of facial complexion in an environment with different artificial light source, the color value of face was extracted and compared with diagnostic conclusion(More)
Numerous researchers have taken the solid step forward towards the objectification research of Traditional Chinese Medicine (TCM) four diagnostic methods. However, it is deficient in studies on information fusion of the four diagnostic methods. We establish four-diagnosis syndrome differentiation model of TCM based on information fusion technology. The(More)
—Syndrome is a unique TCM concept, which is an abstractive collection of symptoms and signs. Several modern algorithms have been applied to classify syndromes, but no satisfied results have been obtained because of the complexity of diagnosis procedure. Support vector machine (SVM) has been found to be very efficient to solve the classification problems,(More)
Objective. This research provides objective and quantitative parameters of the traditional Chinese medicine (TCM) pulse conditions for distinguishing between patients with the coronary heart disease (CHD) and normal people by using the proposed classification approach based on Hilbert-Huang transform (HHT) and random forest. Methods. The energy and the(More)
Objective: We aimed to assess concordance of Zangfu-organs Syndrome Differentiation by evaluating the concordance of successive diagnosis by the same clinician of traditional Chinese medicine (TCM) and that of diagnosis by different clinicians of TCM. Methods: Twenty-five cases were randomly selected and 2 copies were made from the numerical data collected(More)
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