Prescription Function Prediction Using Topic Model and Multilabel Classifiers

Lidong Wang; Yin Zhang; Yun Zhang; Xiaodong Xu; Shihua Cao

doi:10.1155/2017/8279109

Prescription Function Prediction Using Topic Model and Multilabel Classifiers

Evid Based Complement Alternat Med. 2017:2017:8279109. doi: 10.1155/2017/8279109. Epub 2017 Oct 11.

Authors

Lidong Wang¹, Yin Zhang², Yun Zhang³, Xiaodong Xu⁴, Shihua Cao¹

Affiliations

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

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.