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Besides, ARD systems can boost the emerging services of online hospital registration and online medical diagnosis, which require that the outpatients know the correct department first. ARD is a typical problem of text classification. Nevertheless, off-the-shelf tools for text processing may not suit ARD, because the chief complaints of outpatients are generally brief and contain much noisy information. To solve this problem, we propose ARD-K, a deep learning framework incorporating external medical knowledge sources. We also propose a dual-attention mechanism to mitigate the interference of noisy words and knowledge entities. The performance of ARD-K is compared with some off-the-shelf techniques on a real-world dataset. The results demonstrate the effectiveness of ARD-K for the automatic recommendation of departments to outpatients.<\/jats:p>","DOI":"10.3233\/jifs-210599","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T04:54:55Z","timestamp":1620190495000},"page":"3289-3299","source":"Crossref","is-referenced-by-count":3,"title":["Automatic recommendation of medical departments to outpatients based on text analyses and medical knowledge graph"],"prefix":"10.1177","volume":"41","author":[{"given":"Qing","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing, China"}]},{"given":"Wei","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing, China"}]},{"given":"Dai","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-210599_ref1","doi-asserted-by":"crossref","unstructured":"Zitouni, Imed, ed. 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