{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T10:18:06Z","timestamp":1778494686217,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,16]],"date-time":"2020-02-16T00:00:00Z","timestamp":1581811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/info11020106","type":"journal-article","created":{"date-parts":[[2020,2,18]],"date-time":"2020-02-18T10:10:25Z","timestamp":1582020625000},"page":"106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3408-3444","authenticated-orcid":false,"given":"Che-Wen","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shih-Pang","family":"Tseng","sequence":"additional","affiliation":[{"name":"Software Department, Changzhou College of Information Technology, Changzhou 213164, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ta-Wen","family":"Kuan","sequence":"additional","affiliation":[{"name":"School of AI, Guangdong and Taiwan, Foshan University, Foshan 528000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jhing-Fa","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tzafestas, S. 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