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...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.展开更多
基金supported by Hong Kong Research Grants Council under grants No.16202515 and16212516Guangzhou HKUST Fok Ying Tung Research Institute,China Ministry of Science and Technology TCM Special Research Projects Program under grants No.200807011,No.201007002 and No.201407001-8+2 种基金Beijing Science and Technology Program under grant No.Z111107056811040Beijing New Medical Discipline Development Program under grant No.XK100270569Beijing University of Chinese Medicine under grant No.2011-CXTD-23
文摘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.
