{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T17:51:11Z","timestamp":1649008271205},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,15]]},"abstract":"<jats:p>Complexity and domain-specificity make medical text hard to understand for patients and their next of kin. To simplify such text, this paper explored how word and character level information can be leveraged to identify medical terms when training data is limited. We created a dataset of medical and general terms using the Human Disease Ontology from BioPortal and Wikipedia pages. Our results from 10-fold cross validation indicated that convolutional neural networks (CNNs) and transformers perform competitively. The best F score of 93.9% was achieved by a CNN trained on both word and character level embeddings. Statistical significance tests demonstrated that general word embeddings provide rich word representations for medical term identification. Consequently, focusing on words is favorable for medical term identification if using deep learning architectures.<\/jats:p>","DOI":"10.3233\/shti210717","type":"book-chapter","created":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T09:59:26Z","timestamp":1639735166000},"source":"Crossref","is-referenced-by-count":0,"title":["Comparison of Word and Character Level Information for Medical Term Identification Using Convolutional Neural Networks and Transformers"],"prefix":"10.3233","author":[{"given":"Sandaru","family":"Seneviratne","sequence":"first","affiliation":[{"name":"School of Computing, The Australian National University (ANU), Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Artem","family":"Lenskiy","sequence":"additional","affiliation":[{"name":"School of Computing, The Australian National University (ANU), Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher","family":"Nolan","sequence":"additional","affiliation":[{"name":"ANU Medical School and John Curtin School of Medical Research, ANU, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eleni","family":"Daskalaki","sequence":"additional","affiliation":[{"name":"School of Computing, The Australian National University (ANU), Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanna","family":"Suominen","sequence":"additional","affiliation":[{"name":"School of Computing, The Australian National University (ANU), Australia"},{"name":"Data61, Commonwealth Scientific and Industrial Research Organisation, Australia"},{"name":"Department of Computing, University of Turku, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Nurses and Midwives in the Digital Age"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210717","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T09:59:36Z","timestamp":1639735176000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210717"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,15]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210717","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,15]]}}}