A Study on the Named Entity Recognition Method on Symptom Names in the History of Present Illness in Traditional Chinese Medical (TCM) Clinic
10.11842/wst.2017.01.010
- VernacularTitle:面向中医临床现病史文本的命名实体抽取方法研究
- Author:
Yuhu YUAN
;
Xuezhong ZHOU
;
Runshun ZHANG
;
Xiaodong LI
- Keywords:
Traditional Chinese medical clinical data;
medical history of present illness;
conditional random field;
feature template;
named entity recognition
- From:
World Science and Technology-Modernization of Traditional Chinese Medicine
2017;19(1):70-77
- CountryChina
- Language:Chinese
-
Abstract:
Clinical cases of TCM are used as important clinical data to record the whole process of the interaction between doctors and patients in the form of text.However,in the context of big data,there is a lack of research on the use of information covered in clinical cases.Therefore,we studied the method of extracting the symptom term from the history of present illness in TCM clinic in this paper,in order to lay the foundation for the further use of clinical cases.First,twelve thousand,three hundred and sixty-seven history data of present illness were obtained by random selection and expert review.According to the different disease types,they were divided into the two groups of the experiments:4,838 data in the diabetes group,7,529 data in the spleen and stomach disease group and 12,367 data in the mixed or combined group.A glossary of symptom terms covering 22,996 words were compiled.Then,five feature templates,such as sliding window feature,prefix and suffix character and lexical features,were selected.CRFs model was adopted to carry out named entity extraction experiment.As a result,in the open test,the performance of diabetes,spleen and stomach disease and mixed group were (0.83,0.8,0.82),(0.9,0.9,0.89) and (0.88,0.87,0.87),respectively,while the results were (0.83,0.82,0.83),(0.95,0.95,0.95) and (0.93,0.92,0.92) in the ten-fold cross validation.In conclusion,the results showed that the CRFs algorithm was an excellent sequence labeling algorithm and applied to the named entity extraction task of symptom history.
