{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:59Z","timestamp":1755219839643,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"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":[[2025,8,7]]},"abstract":"<jats:p>This study aims to support the expressions adjustment in teaching clinical research protocols through computer assistance by evaluating the accuracy of extracting medical and clinical trial-related terms using existing natural language processing (NLP) tools and the Unified Medical Language System (UMLS) developed by the U.S. National Library of Medicine as a dictionary. Of the 671 unique terms and 939 total terms that did not match the manual validation, 462 unique terms (68.9%) and 518 total terms (55.2%) were deemed improvable through rule-based methods.Addressing these statistical terms could be achieved by adding domain-specific terminology to the dictionary, although variations in expression, such as \u201cdetection rate\u201d versus \u201cstatistical power\u201d or \u201c1-\u03b2,\u201d pose additional challenges.<\/jats:p>","DOI":"10.3233\/shti251266","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:46:33Z","timestamp":1754567193000},"source":"Crossref","is-referenced-by-count":0,"title":["Developing and Assessing the Technical Term Extraction Tools for Teaching Clinical Trial Protocols"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7638-8967","authenticated-orcid":false,"given":"Naoki","family":"Nishimoto","sequence":"first","affiliation":[{"name":"Data Science Center, Promotion Unit, HELIOS, Hokkaido University Hospital, Sapporo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3364-7719","authenticated-orcid":false,"given":"Ayako","family":"Yagahara","sequence":"additional","affiliation":[{"name":"Department of Radiological Technology, Hokkaido University of Science, Sapporo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5664-9616","authenticated-orcid":false,"given":"Ken","family":"Sakushima","sequence":"additional","affiliation":[{"name":"Clinical Research and Innovation Center, Promotion Unit, HELIOS, Hokkaido University Hospital, Sapporo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251266","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:46:33Z","timestamp":1754567193000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251266"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251266","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}