DocumentCode :
1800047
Title :
TCM syndrome classification of AIDS based on Manifold ranking
Author :
Yufeng Zhao ; Lin Luo ; Liyun He ; Guanli Song ; Baoyan Liu ; Qi Xie ; Xiaoping Zhang ; Wang Jian ; Xianghong Jing
Author_Institution :
Inst. of Basic Res. in Clinical Med., China Acad. of Chinese Med. Sci., Beijing, China
fYear :
2014
fDate :
9-12 Dec. 2014
Firstpage :
1
Lastpage :
5
Abstract :
Treatment based on the syndrome differentiation is the key of Traditional Chinese Medicine (TCM) treating the disease of acquired immune deficiency syndrome (AIDS). Therefore, a feasible way of improving the clinical therapy effectiveness is to correctly explore the syndrome classifications. Recently, more and more AIDS researchers are focused on exploring the syndrome classifications. In this paper, a novel data mining method based on Manifold Ranking (MR) is proposed to analyze the syndrome classifications for the disease of AIDS. Compared with the previous methods, three weaknesses, which are linear relation of the clinical data, mutually exclusive symptoms among different syndromes, confused application of expert knowledge, are avoided so as to effectively exploit the latent relation between syndromes and symptoms. Better performance of syndrome classifications is able to be achieved according to the experimental results and the clinical experts.
Keywords :
data analysis; data mining; diseases; medical computing; pattern classification; AIDS; TCM syndrome classification; acquired immune deficiency syndrome; clinical data; clinical therapy; data mining method; disease treatment; expert knowledge; latent relation; manifold ranking; mutually exclusive symptoms; syndrome differentiation; traditional Chinese medicine; Acquired immune deficiency syndrome; Blood; Human immunodeficiency virus; Lungs; Medical diagnostic imaging; Acquired Immune Deficiency Syndrome (AIDS); Data Mining; Manifold Ranking; Syndrome classifications; Traditional Chinese Medicine (TCM);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence in Big Data (CIBD), 2014 IEEE Symposium on
Conference_Location :
Orlando, FL
Type :
conf
DOI :
10.1109/CIBD.2014.7011534
Filename :
7011534
Link To Document :
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