title: A data-driven method for syndrome type identification and classification in traditional Chinese medicine.
abstract: 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.
authors.affiliation: Department of Computer Science and Engineering, the Hong Kong University of Science and Technology, Hong Kong, China.
authors.forename: Nevin Lianwen
authors.initials: NL
authors.lastname: Zhang
authors.affiliation: Department of Neurology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, China.
authors.forename: Chen
authors.initials: C
authors.lastname: Fu
authors.affiliation: Department of Computer Science and Engineering, the Hong Kong University of Science and Technology, Hong Kong, China.
authors.forename: Teng Fei
authors.initials: TF
authors.lastname: Liu
authors.affiliation: Department of Neurology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, China.
authors.forename: Bao-Xin
authors.initials: BX
authors.lastname: Chen
authors.affiliation: Department of Mathematics and Information Technology, the Education University of Hong Kong, Hong Kong, China.
authors.forename: Kin Man
authors.initials: KM
authors.lastname: Poon
authors.affiliation: Department of Computer Science and Engineering, the Hong Kong University of Science and Technology, Hong Kong, China.
authors.forename: Pei Xian
authors.initials: PX
authors.lastname: Chen
authors.affiliation: Department of Neurology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, China.
authors.forename: Yun-Ling
authors.initials: YL
authors.lastname: Zhang
mesh_terms: D003625:Data Collection; D003627:Data Interpretation, Statistical; D003937:Diagnosis, Differential; D006801:Humans; D008516:Medicine, Chinese Traditional