Outpatient Text Classification Using LSTM for Robot-assisted Servicing in Hospital

In this paper, we propose a long short-term memory (LSTM) model for the ASUS Zenbo service robot that has the ability to classify outpatient categories according to textual content. 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. In the implementation of the proposed method, dialog texts of users in the Taiwan E Hospital were collected as the training dataset. Through natural language processing (NLP), the information in the dialog text was extracted, sorted, and converted to internal representations that were used to train the LSTM deep learning model. The experimental results veried the ability of the robot respond to questions autonomously through acquired causal knowledge.