NDLI: TCM clinic records data mining approaches based on weighted-LDA and multi-relationship LDA model

Content Provider	Springer Nature Link
Author	Lin, Fan Xiahou, Jianbing Xu, Zhuxiang
Copyright Year	2016
Abstract	As an important part of traditional medicine, TCM (Traditional Chinese Medicine) has unique and distinct clinical effects in the aspect of disease diagnosis and treatment. Thousands of years of TCM treatment has accumulated abundant clinical data and medical literatures, including valued TCM theories and clinical practice rules. Researchers have conducted various methods such as clustering analysis, association rules and regression analysis to study TCM theory. However, none of them could reflect well the semantic complexity of TCM and systemic characteristics of TCM treatment. This paper conducted a research on the inherent rules of TCM clinic records with a topic model. On the basis of LDA model, weighted mechanism was adopted for each feature word to improve the distinguishing ability and interpretability between the topics. Meanwhile, the modeled topic is taken as the feature for the classification of SVM (Support Vector Machine) to improve the classification accuracy. The topic number of LDA topic model is confirmed by the KL distance and similarity between the topics. After analyzing the relationship between topic model and TCM differentiation and treatment, MULTI-RELATIONSHIP Topics LDA MODEL was proposed on the basis of LDA model and Author-topic model to automatically extract the topic structures between the four parties and explore the relationship of the multiple parties with clinical significance. In the meantime, relevancy between the parties and the feature word weighted mechanism are used to improve the MULTI-RELATIONSHIP Topics LDA MODEL and the classification accuracy of the topics. The experiments showed that analysis of clinical data with topic model can extract TCM treatment rules and provide a novel theoretical method for TCM clinical research.
Starting Page	14203
Ending Page	14232
Page Count	30
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	75
Issue Number	22
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2016-04-13
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Topic model Latent Dirichlet Allocation(LDA) Multi-relationship Data mining Author-Topic model TF-IDF Traditional Chinese Medicine(TCM) Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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