{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T11:32:37Z","timestamp":1769859157511,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643682648","type":"print"},{"value":"9781643682655","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"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":[[2022,6,6]]},"abstract":"<jats:p>The objective of this study was to develop a hybrid method and perform an initial evaluation of mappings from the International Statistical Classification of Diseases, 10th revision, Chinese version (ICD-10-CN) to the Systematized Nomenclature of Medicine \u2013 Clinical Terms (SNOMED-CT). The methods used to perform mapping include reusing existing mappings, term similarity modeling for automatic mapping and manual review. We evaluated the results of automatic mapping and the coverage of the maps between two terminologies. Experimental results demonstrated that fine-tuning the pre-trained biomedical language model of PubmedBERT obtained the optimal performance, with a precision of 0.859, a recall of 0.773, and a F1 of 0.814. 100% 4-digit code ICD-10-CN terms were mapped to SNOMED-CT terms through exsit code mappings. Around 42.41% randomly selected 6-digit code ICD-10-CN terms had exact matches to corresponding SNOMED-CT terms, and we did not find appropriate SNOMED-CT terms for ICD grouping terms.<\/jats:p>","DOI":"10.3233\/shti220028","type":"book-chapter","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:29:49Z","timestamp":1654594189000},"source":"Crossref","is-referenced-by-count":2,"title":["Cross-Language Terminology Mapping Between ICD-10-CN and SNOMED-CT"],"prefix":"10.3233","author":[{"given":"Na","family":"Hong","sequence":"first","affiliation":[{"name":"Digital Health China Technologies Co. Ltd., Beijing, China"}]},{"given":"Yaoyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Melax Technologies, Inc, Houston, TX, USA"}]},{"given":"Yuankai","family":"Ren","sequence":"additional","affiliation":[{"name":"Jiangnan University, Suzhou, China"}]},{"given":"Li","family":"Hou","sequence":"additional","affiliation":[{"name":"Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China"}]},{"given":"Changran","family":"Wang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"Real World Solutions, IQVIA, Durham, NC, USA"}]},{"given":"Mui","family":"Van Zandt","sequence":"additional","affiliation":[{"name":"Real World Solutions, IQVIA, Durham, NC, USA"}]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2021: One World, One Health \u2013 Global Partnership for Digital Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220028","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:29:50Z","timestamp":1654594190000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"ISBN":["9781643682648","9781643682655"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220028","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]}}}