A data-driven method for syndrome type identification and classification in traditional Chinese medicine / 中西医结合学报
Journal of Integrative Medicine
; (12): 110-123, 2017.
Article
em En
| WPRIM
| ID: wpr-346269
Biblioteca responsável:
WPRO
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.
Texto completo:
1
Índice:
WPRIM
Assunto principal:
Coleta de Dados
/
Interpretação Estatística de Dados
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Diagnóstico Diferencial
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Medicina Tradicional Chinesa
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Revista:
Journal of Integrative Medicine
Ano de publicação:
2017
Tipo de documento:
Article