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Author:

Liu, Zheng (Liu, Zheng.) | Fang, Liying (Fang, Liying.) | Yu, Mingwei (Yu, Mingwei.) | Wang, Pu (Wang, Pu.) (Scholars:王普)

Indexed by:

CPCI-S

Abstract:

Relevance exists in Traditional Chinese Medicine(TCM) clinical symptoms. Their different combinations reflect different effects. Focusing on these characteristics, an univariate analysis method based on the factor analysis and clustering(FACUA) is proposed. First, the independent common factors extracted from the correlative multivariable are used to establish the eigenvectors of symptoms for patients. Then, the symptom patterns are discovered from the gathered similar symptoms combination. The method is verified by the patients with advanced NSCLC(non-small cell lung cancer) from Beijing Hospital of Traditional Chinese Medicine. The experimental result shows that the FACUA method can deal with the TCM clinical symptoms and analyze the relationship between the TCM clinical symptoms and the tumor progression. The FACUA method can improve the universal applicability of the univariate analysis in TCM clinical symptoms.

Keyword:

Clustering Clinical Symptoms Factor Analysis Univariate Analysis

Author Community:

  • [ 1 ] [Liu, Zheng]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Fang, Liying]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Wang, Pu]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 4 ] [Yu, Mingwei]CPUMS, Beijing Hosp Tradit Chinese Med, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Liu, Zheng]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China

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Source :

NONLINEAR MATHEMATICS FOR UNCERTAINTY AND ITS APPLICATIONS

ISSN: 1867-5662

Year: 2011

Volume: 100

Page: 533-,

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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