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Latent Structure Analysis and Syndrome Differentiation for Integration of Traditional Chinese Medicine and Western Medicine (III):Establishment of Classification Rules / 世界科学技术-中医药现代化
World Science and Technology-Modernization of Traditional Chinese Medicine ; (12): 723-730, 2014.
Article in Chinese | WPRIM | ID: wpr-671770
ABSTRACT

Objective:

In China, doctors at TCM hospitals and clinics often divide patients with a Western medicine (WM) disease into several syndrome classes from the TCM perspective and treat patients in different classes using different principles. A key problem is how to carry out the classification properly. We propose an evidence-based ap-proach for solving the problem where evidence is obtained by analyzing unlabeled symptom data using latent tree models.

Method:

In previous work, we have shown how latent tree analysis of symptom data can be used to identify TCM syndrome classes among patients with a WM disease. In the paper, we investigate how to establish classification rules for distinguishing between the classes.

Results:

We have applied the method to a data set about Vascular Mild Cognitive Impairment that involves 93 symptoms and 803 patients. Nine syndrome types are identified, along with the corresponding classification rules.

Conclusions:

An evidence-based approach to the TCM patient classification prob-lem has been developed. The approach can be used to answer the following questions about a WM disease What TCM syndrome classes are there? What are the sizes of the classes? What are the statistical characteristics of each class? How can one differentiate between the different classes?

Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: World Science and Technology-Modernization of Traditional Chinese Medicine Year: 2014 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: World Science and Technology-Modernization of Traditional Chinese Medicine Year: 2014 Type: Article