Table of Contents Author Guidelines Submit a Manuscript
Journal of Healthcare Engineering
Volume 2017 (2017), Article ID 4898963, 16 pages
Research Article

A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text

1Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310000, China
2Sir Run Run Shaw Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310000, China
3Zhejiang University International Hospital, Hangzhou, Zhejiang Province 310000, China
4National Center for Advancing Translation Sciences, National Institutes of Health, 9800 Medical Center Drive, Building C, Room 312, Rockville, MD 20850, USA
5Hangzhou Medical College, Hangzhou, Zhejiang Province 310000, China
6Peking University Center for Medical Informatics Center, Beijing 100191, China
7Southwest Medical University, Luzhou, Sichuan Province 646000, China

Correspondence should be addressed to Jianbo Lei

Received 31 March 2017; Revised 5 May 2017; Accepted 16 May 2017; Published 5 July 2017

Academic Editor: Jiang Bian

Copyright © 2017 Jun Liang 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.


Medical entity recognition, a basic task in the language processing of clinical data, has been extensively studied in analyzing admission notes in alphabetic languages such as English. However, much less work has been done on nonstructural texts that are written in Chinese, or in the setting of differentiation of Chinese drug names between traditional Chinese medicine and Western medicine. Here, we propose a novel cascade-type Chinese medication entity recognition approach that aims at integrating the sentence category classifier from a support vector machine and the conditional random field-based medication entity recognition. We hypothesized that this approach could avoid the side effects of abundant negative samples and improve the performance of the named entity recognition from admission notes written in Chinese. Therefore, we applied this approach to a test set of 324 Chinese-written admission notes with manual annotation by medical experts. Our data demonstrated that this approach had a score of 94.2% in precision, 92.8% in recall, and 93.5% in F-measure for the recognition of traditional Chinese medicine drug names and 91.2% in precision, 92.6% in recall, and 91.7% F-measure for the recognition of Western medicine drug names. The differences in F-measure were significant compared with those in the baseline systems.