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Journal of Integrative Medicine ›› 2017, Vol. 15 ›› Issue (2): 110-123.doi: 10.1016/S2095-4964(17)60328-5

Special Issue: Traditional Chinese Medicine

• Methodology • Previous Articles     Next Articles

A data-driven method for syndrome type identification and classification in traditional Chinese medicine

Nevin Lianwen Zhanga, Chen Fub, Teng Fei Liua, Bao-xin Chenb, Kin Man Poonc, Pei Xian Chena, Yunling Zhangb   

  1. a Department of Computer Science and Engineering, the Hong Kong University of Science and Technology, Hong Kong, China 
    b Department of Neurology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, China 
    c Department of Mathematics and Information Technology, the Education University of Hong Kong, Hong Kong, China
  • Received:2016-08-30 Accepted:2016-11-07 Online:2017-03-15 Published:2017-03-15
  • Contact: Prof. Nevin Lianwen Zhang; E-mail: lzhang@cse.ust.hk. Prof. Yun-ling Zhang; E-mail: yunlingzhang2004@163.com

The efficacy of traditional Chinese medicine (TCM) treatments for Western medicine (WM) diseases relies heavily on the proper classification of patients into TCM syndrome types. The authors developed a data-driven method for solving the classification problem, where syndrome types were identified and quantified based on statistical patterns detected in unlabeled symptom survey data. The new method is a generalization of latent class analysis (LCA), which has been widely applied in WM research to solve a similar problem, i.e., to identify subtypes of a patient population in the absence of a gold standard. A well-known weakness of LCA is that it makes an unrealistically strong independence assumption. The authors relaxed the assumption by first detecting symptom co-occurrence patterns from survey data and used those statistical patterns instead of the symptoms as features for LCA. This new method consists of six steps: data collection, symptom co-occurrence pattern discovery, statistical pattern interpretation, syndrome identification, syndrome type identification and syndrome type classification. A software package called Lantern has been developed to support the application of the method. The method was illustrated using a data set on vascular mild cognitive impairment.

Key words: medicine, Chinese traditional, syndrome, syndrome classification, latent tree analysis, symptom co-occurrence patterns, patient clustering, stand syndrome differentiation

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