Topic Model for Chinese Medicine Diagnosis and Prescription Regularities Analysis:Case on Diabetes  

作　　者：张小平 周雪忠 黄厚宽 冯奇 陈世波 刘保延 

机构地区：[1]School of Computer and Information Technology,Beijing Jiaotong University [2]Guang'anmen Hospital,China Academy of Chinese Medical Sciences [3]China Academy of Chinese Medical Sciences

出　　处：《Chinese Journal of Integrative Medicine》2011年第4期307-313,共7页中国结合医学杂志（英文版）

基　　金：Supported by Scientific Breakthrough Program of Beijing Municipal Science & Technology Commission,China(No. D08050703020803,No.D08050703020804);China Key Technologies R&D Programme(No.2007BA110B06-01);Major State Basic Research Development Program of China (973 Program,No.2006CB504601);National Nature Science Foundation of China(No.90709006);National Science and Technology Major Project of the Ministry of Science and Technology of China(No.2009ZX10005-019)

摘　　要：Induction of common knowledge or regularities from large-scale clinical data is a vital task for Chinese medicine（CM）.In this paper,we propose a data mining method,called the Symptom-Herb-Diagnosis topic（SHDT） model,to automatically extract the common relationships among symptoms,herb combinations and diagnoses from large-scale CM clinical data.The SHDT model is one of the multi-relational extensions of the latent topic model,which can acquire topic structure from discrete corpora（such as document collection） by capturing the semantic relations among words.We applied the SHDT model to discover the common CM diagnosis and treatment knowledge for type 2 diabetes mellitus（T2DM） using 3 238 inpatient cases.We obtained meaningful diagnosis and treatment topics（clusters） from the data,which clinically indicated some important medical groups corresponding to comorbidity diseases（e.g.,heart disease and diabetic kidney diseases in T2DM inpatients）.The results show that manifestation sub-categories actually exist in T2DM patients that need specific,individualised CM therapies.Furthermore,the results demonstrate that this method is helpful for generating CM clinical guidelines for T2DM based on structured collected clinical data.Induction of common knowledge or regularities from large-scale clinical data is a vital task for Chinese medicine（CM）.In this paper,we propose a data mining method,called the Symptom-Herb-Diagnosis topic（SHDT） model,to automatically extract the common relationships among symptoms,herb combinations and diagnoses from large-scale CM clinical data.The SHDT model is one of the multi-relational extensions of the latent topic model,which can acquire topic structure from discrete corpora（such as document collection） by capturing the semantic relations among words.We applied the SHDT model to discover the common CM diagnosis and treatment knowledge for type 2 diabetes mellitus（T2DM） using 3 238 inpatient cases.We obtained meaningful diagnosis and treatment topics（clusters） from the data,which clinically indicated some important medical groups corresponding to comorbidity diseases（e.g.,heart disease and diabetic kidney diseases in T2DM inpatients）.The results show that manifestation sub-categories actually exist in T2DM patients that need specific,individualised CM therapies.Furthermore,the results demonstrate that this method is helpful for generating CM clinical guidelines for T2DM based on structured collected clinical data.

关 键 词：latent Dirichlet allocation Author-Topic model Dirichlet priori Chinese medicine Symptom-Herb-Diagnosis topic model 

分 类 号：R259[医药卫生—中西医结合]