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.
Highlights
With the outpatient text classification system, users can talk about their situation to the service robot and the robot can tell them which clinic they should register with
We designed an attention-based bidirectional long-short term memory (LSTM) model to deal with the problem of text classification
The focus of this study was on unstructured data, a discussion of text classification in natural language processing (NLP), and adopting LSTM with term frequency–inverse document frequency (TF–IDF) to improve semantic cognition and computing
Summary
There has been increasing interest in integrating and applying techniques drawn from the fields of artificial intelligence (AI) and robotics [1], including in vision [2], navigation [3], manipulation [4], emotion recognition [5], speech recognition [6], and natural language processing (NLP) [7]. Improvements in intelligent control systems and precision sensors have resulted in a wide variety of robot applications in the services field, including in health care [8], tourism [9], markets [10], education [1], and at home [11]. With the rapid development of robotic technologies, service robots have gradually entered into and are improving the quality of people’s daily lives [12]. Automatic consultation can reduce manpower and improve service quality. People need intelligent, safe, and effective service from service robots
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