JISE


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Journal of Information Science and Engineering, Vol. 37 No. 2, pp. 365-379


Outpatient Text Classification System Using LSTM


CHE-WEN CHEN1, SHIH-PANG TSENG2 AND JHING-FA WANG1
1,3Department of Electrical Engineering
National Cheng Kung University
Tainan, 701 Taiwan

2School of Software
Changzhou College of Information Technology
Changzhou, 213164 P.R. China
E-mail:
1kfcmax300@gmail.com; 2tsengshihpang@ccit.js.cn; 3jameswangjf@gmail.com


Outpatient text classification is an important problem in medical natural language processing. Existing research has conventionally focused on rule-based or knowledge-sourcebased feature engineering, but only a few studies have utilized the effective feature learning capabilities of deep learning methods. A long short-term memory (LSTM) model for the outpatient text classification system was proposed in this research. The system has the ability to classify outpatient categories according to textual content on website Taiwan E Hospital. The experimental results showed that our system has very well in the task. The success of the LSTM model applications in the outpatient system provide users to inquire about their health status as references.


Keywords: text classification, natural language processing, smart healthcare, service robot system

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