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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2017, Vol. 11 Issue (3) : 432-439    https://doi.org/10.1007/s11684-017-0511-1
RESEARCH ARTICLE
A novel classification method for aid decision of traditional Chinese patent medicines for stroke treatment
Yufeng Zhao1,2(), Bo Liu3, Liyun He1, Wenjing Bai1, Xueyun Yu1, Xinyu Cao1,2, Lin Luo1, Peijing Rong4, Yuxue Zhao4, Guozheng Li2,5, Baoyan Liu2,5()
1. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
2. National Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
3. Qingdao Hiser Hospital, Qingdao 266033, China
4. Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing 100700, China
5. China Academy of Chinese Medical Sciences, Beijing 100700, China
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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     
Corresponding Author(s): Yufeng Zhao,Baoyan Liu   
Just Accepted Date: 20 March 2017   Online First Date: 17 May 2017    Issue Date: 29 August 2017
 Cite this article:   
Yufeng Zhao,Bo Liu,Liyun He, et al. A novel classification method for aid decision of traditional Chinese patent medicines for stroke treatment[J]. Front. Med., 2017, 11(3): 432-439.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-017-0511-1
https://academic.hep.com.cn/fmd/EN/Y2017/V11/I3/432
Fig.1  Flowchart of data acquisitions.
DataSourcesExamples
Symptom scaleReferences andclinical practiceVomit, cough, etc.
Disease informationElectronic recordsHistory of disease, heredity, etc.
Tongue and pulseFour diagnostic instrumentsColor, location, etc.
Physical and chemicalLIS or HISHemoglobin, blood pressure, etc.
Tab.1  Summary of stroke patients
Fig.2  Flowchart of aid-decision model based on Manifold Ranking
SymbolsDefinition
p={pki,k=1,...,K;i=1,...,M}Set of patients
C={cj,j=1,...,N}Set of Chinese patent medicine
W={wij,i=1,...,M;j=1,...,M}Set of weight probability
Tab.2  Definition of the symbols
Data sourceData typeSimilarity measurement
Symptom scaleBinary distributionHamming distance
Disease informationTxtHamming distance
Tongue and pulseNormal distributionEuclidean distance
Physical and chemicalNormal distributionEuclidean distance
MedicineBinary distributionHamming distance
Tab.3  Similarity definition of the various data
Inputs
Similarity matrix of patients Ms
Initial weight probability matrix Mwfor all medicine classes
Outputs
Final weight probability matrix Mw*
Procedure
Step 1: Collecting the similarity matrix of the patients Ms
Step 2: Normalizing the similarity matrix with Eq. (2)
Mnorm=MD12MsMD12,
where MD is a diagonal matrix and MDii is the sum of the ith row of the weight probability matrixMw
Step 3: Iterating Eq. (3) until the converged solution Mw* is achieved
Mw*(t+1)=αMnormMw*(t)+(1α)Mw(0),
where t is the number of iteration α[0,1], and Mw(0) is the initial weight probability matrix
Step 4: Deciding the initial medicine list of each patient based on the final weight probability matrix Mw*
Tab.4  Propagating process of the medicine classes
Parameters of modelsResults
Number of training data80
Number of testing data35
Average precision of medicine85.79%
Average recall of medicine61.44%
Average precision of patients62.69%
Tab.5  Performance of the aid-decision model based on Manifold Ranking
Fig.3  Effectiveness of different indicators.
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