DocumentCode :
2506024
Title :
Using link topic model to analyze traditional Chinese Medicine Clinical symptom-herb regularities
Author :
Jiang, Zaixing ; Zhou, Xuezhong ; Zhang, Xiaoping ; Chen, Shibo
Author_Institution :
Sch. of Comput. & Inf. Technol., Beijing Jiaotong Univ., Beijing, China
fYear :
2012
fDate :
10-13 Oct. 2012
Firstpage :
15
Lastpage :
18
Abstract :
Traditional Chinese Medicine (TCM) is a clinical medicine, which focuses on human physiology, pathology, diagnosis and treatment of diseases. Numerous clinical practice and theory research in the TCM field have accumulated huge amount of data. These data include TCM basic databases, TCM literature, as well as a large number of databases or data warehouse on TCM clinical diagnoses and treatment. More and more people pay attention to the discovery of hidden regularities of TCM clinical data. In recent years, topic model has been popularly used for text analysis and information retrieval by extracting latent and significant topics from corpus. In this paper, we apply the Link Latent Dirichlet Allocation (LinkLDA), to automatically extract the latent topic structures which contain the information of both symptoms and their corresponding herbs. By experimental results, the latent topic with symptoms and their corresponding herbs show clinical meaningful results. Furthermore, the model is also compared with other topic models, such as author-topic model, and the result of LinkLDA got better results.
Keywords :
knowledge engineering; medical computing; patient treatment; text analysis; Link Latent Dirichlet Allocation; LinkLDA; TCM clinical symptom-herb regularities; author-topic model; clinical medicine; disease diagnosis; disease pathology; disease treatment; human physiology; information retrieval; latent topic structures; link topic model; text analysis; traditional Chinese medicine; Analytical models; Data mining; Data models; Diabetes; Diseases; Medical diagnostic imaging; Probability distribution; Author-topic model(AT); Latent Dirichlet allocation (LDA); Link latent Dirichlet allocation (LinkLDA); Traditional Chinese Medicine (TCM);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
e-Health Networking, Applications and Services (Healthcom), 2012 IEEE 14th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4577-2039-0
Electronic_ISBN :
978-1-4577-2038-3
Type :
conf
DOI :
10.1109/HealthCom.2012.6380057
Filename :
6380057
Link To Document :
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