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Evidence-Based Complementary and Alternative Medicine
Volume 2017 (2017), Article ID 8279109, 10 pages
https://doi.org/10.1155/2017/8279109
Research Article

Prescription Function Prediction Using Topic Model and Multilabel Classifiers

1Qianjiang College, Hangzhou Normal University, Hangzhou 310018, China
2College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China
3Zhejiang University of Media and Communications, Hangzhou, Zhejiang 310018, China
4Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, China

Correspondence should be addressed to Lidong Wang

Received 14 September 2016; Accepted 13 June 2017; Published 11 October 2017

Academic Editor: Kenji Watanabe

Copyright © 2017 Lidong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.