A novel classification method for aid decision of traditional Chinese patent medicines for stroke treatment

Abstract

Traditional Chinese patent medicines are widely used to treat stroke because it has good efficacy in the clinical environment. However, because of the lack of knowledge on traditional Chinese patent medicines, many Western physicians, who are accountable for the majority of clinical prescriptions for such medicine, are confused with the use of traditional Chinese patent medicines. Therefore, the aid-decision method is critical and necessary to help Western physicians rationally use traditional Chinese patent medicines. In this paper, Manifold Ranking is employed to develop the aid-decision model of traditional Chinese patent medicines for stroke treatment. First, 115 stroke patients from three hospitals are recruited in the cross-sectional survey. Simultaneously, traditional Chinese physicians determine the traditional Chinese patent medicines appropriate for each patient. Second, particular indicators are explored to characterize the population feature of traditional Chinese patent medicines for stroke treatment. Moreover, these particular indicators can be easily obtained by Western physicians and are feasible for widespread clinical application in the future. Third, the aid-decision model of traditional Chinese patent medicines for stroke treatment is constructed based on Manifold Ranking. Experimental results reveal that traditional Chinese patent medicines can be differentiated. Moreover, the proposed model can obtain high accuracy of aid decision.

Keywords

traditional Chinese patent medicines / stroke / aid decision / data mining / manifold ranking

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Nos. 81674101 and 81202858), National Key Technology Support Program (No. 2012BAI25B02), Self-selected subject of China Academy of Chinese Medical Sciences (Nos. ZZ05003, ZZ03090, and Z0217), Beijing Key Laboratory of Advanced Information Science Network Technology (No. XDXX1306), and Autonomous Grant from Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences (No. Z254).

Compliance with ethics guidelines

Yufeng Zhao, Bo Liu, Liyun He, Wenjing Bai, Xueyun Yu, Xinyu Cao, Lin Luo, Peijing Rong, Yuxue Zhao, Guozheng Li, and Baoyan Liu declare that they have no conflict of interest. This manuscript involves clinical data from a research protocol and the ethics committee had approved the research protocol.

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