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Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital.
- Source :
-
Information (2078-2489) . Feb2020, Vol. 11 Issue 2, p106. 1p. - Publication Year :
- 2020
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20782489
- Volume :
- 11
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Information (2078-2489)
- Publication Type :
- Academic Journal
- Accession number :
- 142068920
- Full Text :
- https://doi.org/10.3390/info11020106