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Open AccessArticle

Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital

1
Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
2
Software Department, Changzhou College of Information Technology, Changzhou 213164, China
3
School of AI, Guangdong and Taiwan, Foshan University, Foshan 528000, China
*
Author to whom correspondence should be addressed.
Information 2020, 11(2), 106; https://doi.org/10.3390/info11020106
Received: 14 January 2020 / Revised: 7 February 2020 / Accepted: 11 February 2020 / Published: 16 February 2020
(This article belongs to the Special Issue Natural Language Processing in Healthcare and Medical Informatics)
In general, patients who are unwell do not know with which outpatient department they should register, and can only get advice after they are diagnosed by a family doctor. This may cause a waste of time and medical resources. In this paper, we propose an attention-based bidirectional long short-term memory (Att-BiLSTM) model for service robots, which has the ability to classify outpatient categories according to textual content. With the outpatient text classification system, users can talk about their situation to a service robot and the robot can tell them which clinic they should register with. In the implementation of the proposed method, dialog text of users in the Taiwan E Hospital were collected as the training data set. Through natural language processing (NLP), the information in the dialog text was extracted, sorted, and converted to train the long-short term memory (LSTM) deep learning model. Experimental results verify the ability of the robot to respond to questions autonomously through acquired casual knowledge. View Full-Text
Keywords: text classification; health care; service robot; natural language processing text classification; health care; service robot; natural language processing
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Chen, C.-W.; Tseng, S.-P.; Kuan, T.-W.; Wang, J.-F. Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital. Information 2020, 11, 106.

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