Supervised methods for symptom name recognition in free-text clinical records of traditional Chinese medicine: an empirical study.
Clinical records of traditional Chinese medicine (TCM) are documented by TCM doctors during their routine diagnostic work. These records contain abundant knowledge and reflect the clinical experience of TCM doctors. In recent years, with the modernization of TCM clinical practice, these clinical records have begun to be digitized. Data mining (DM) and machine learning (ML) methods provide an opportunity for researchers to discover TCM regularities buried in the large volume of clinical records. There has been some work on this problem. Existing methods have been validated on a limited amount of manually well-structured data. However, the contents of most fields in the clinical records are unstructured. As a result, the previous methods verified on the well-structured data will not work effectively on the free-text clinical records (FCRs), and the FCRs are, consequently, required to be structured in advance. Manually structuring the large volume of TCM FCRs is time-consuming and labor-intensive, but the development of automatic methods for the structuring task is at an early stage. Therefore, in this paper, symptom name recognition (SNR) in the chief complaints, which is one of the important tasks to structure the FCRs of TCM, is carefully studied. The SNR task is reasonably treated as a sequence labeling problem, and several fundamental and practical problems in the SNR task are studied, such as how to adapt a general sequence labeling strategy for the SNR task according to the domain-specific characteristics of the chief complaints and which sequence classifier is more appropriate to solve the SNR task. To answer these questions, a series of elaborate experiments were performed, and the results are explained in detail.
Department of Computer Science, Sichuan University, Chengdu, Sichuan 610064, PR China. Electronic address: wangyaq2204_cn@hotmail.com.
Yaqiang
Y
Wang
Department of Computer Science, Sichuan University, Chengdu, Sichuan 610064, PR China. Electronic address: yuzhonghua@scu.edu.cn.
Zhonghua
Z
Yu
Department of Computer Science, Sichuan University, Chengdu, Sichuan 610064, PR China. Electronic address: cl@scu.edu.cn.
Li
L
Chen
School of Fundamental Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, PR China. Electronic address: tcmhero@126.com.
Yunhui
Y
Chen
Department of Computer Science, Sichuan University, Chengdu, Sichuan 610064, PR China. Electronic address: lygpapers@yahoo.com.cn.
Yiguang
Y
Liu
No. 1 Clinical Hospital, Beihua University, Jilin, Jilin 132011, PR China. Electronic address: 21224498@qq.com.
Xiaoguang
X
Hu
Department of Preclinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, PR China. Electronic address: cdtcm@163.com.
Yongguang
Y
Jiang
D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining; D004365:Drugs, Chinese Herbal / Q000627:therapeutic use; D006801:Humans; D019359:Knowledge; D007802:Language; D008490:Medical Informatics / Q000379:methods*; D008516:Medicine, Chinese Traditional / Q000379:methods*; D012984:Software
D004365:Drugs, Chinese Herbal
Free-text clinical records; Natural language processing; Supervised sequence classification; Symptom name recognition; Traditional Chinese medicine