Prescription Function Prediction Using Topic Model and Multilabel Classifiers
Joint Authors
Wang, Lidong
Zhang, Yin
Zhang, Yun
Xu, Xiaodong
Cao, Shihua
Source
Evidence-Based Complementary and Alternative Medicine
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-11
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Determining a prescription’s function is one of the challenging problems in Traditional Chinese Medicine (TCM).
In past decades, TCM has been widely researched through various methods in computer science, but none concentrates on the prediction method for a new prescription’s function.
In this study, two methods are presented concerning this issue.
The first method is based on a novel supervised topic model named Label-Prescription-Herb (LPH), which incorporates herb-herb compatibility rules into learning process.
The second method is based on multilabel classifiers built by TFIDF features and herbal attribute features.
Experiments undertaken reveal that both methods perform well, but the multilabel classifiers slightly outperform LPH-based method.
The prediction results can provide valuable information for new prescription discovery before clinical test.
American Psychological Association (APA)
Wang, Lidong& Zhang, Yin& Zhang, Yun& Xu, Xiaodong& Cao, Shihua. 2017. Prescription Function Prediction Using Topic Model and Multilabel Classifiers. Evidence-Based Complementary and Alternative Medicine،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1154708
Modern Language Association (MLA)
Wang, Lidong…[et al.]. Prescription Function Prediction Using Topic Model and Multilabel Classifiers. Evidence-Based Complementary and Alternative Medicine No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1154708
American Medical Association (AMA)
Wang, Lidong& Zhang, Yin& Zhang, Yun& Xu, Xiaodong& Cao, Shihua. Prescription Function Prediction Using Topic Model and Multilabel Classifiers. Evidence-Based Complementary and Alternative Medicine. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1154708
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1154708