Name
Title | ||
|---|---|---|
Outpatient Text Classification Using Attention-Based Bidirectional Lstm For Robot-Assisted Servicing In Hospital |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/info11020106 | INFORMATION |
Keywords | Field | DocType |
text classification, health care, service robot, natural language processing | Training set,Health care,Dialog box,Computer science,Human–computer interaction,Artificial intelligence,Deep learning,Casual,Robot,Machine learning,Term memory,Service robot | Journal |
Volume | Issue | Citations |
11 | 2 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Che-Wen Chen | 1 | 3 | 0.80 |
| Shih-Pang Tseng | 2 | 1 | 1.03 |
| Ta-Wen Kuan | 3 | 1 | 0.35 |
| Jhing-fa Wang | 4 | 982 | 114.31 |