{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:44:50Z","timestamp":1776185090665,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Ministry of Science and ICT"},{"name":"ICT Consilience Creative Program","award":["IITP-2020-2011-1-00783"],"award-info":[{"award-number":["IITP-2020-2011-1-00783"]}]},{"name":"Institute for Information & Communications Technology Planning & Evaluation"},{"DOI":"10.13039\/501100007053","name":"Korea Institute of Energy Technology Evaluation and Planning","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007053","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korean government","award":["20202020800030"],"award-info":[{"award-number":["20202020800030"]}]},{"name":"Holistic Performance Testing and Evaluation Methods"},{"name":"Field Verifications"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such schemes require expensive human supervision. This study aims to construct a trainable system that can automate the process of semantic interinstitutional code mapping.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>To automate mapping between source and target codes, we compute the embedding-based semantic similarity between corresponding descriptive sentences. We also implement a systematic approach for preparing training data for similarity computation. Experimental results are compared to traditional word-based mappings.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The proposed model is compared against the state-of-the-art automated matching system, which is called Usagi, of the Observational Medical Outcomes Partnership common data model. By incorporating multiple negative training samples per positive sample, our semantic matching method significantly outperforms Usagi. Its matching accuracy is at least 10% greater than that of Usagi, and this trend is consistent across various top-k measurements.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>The proposed deep learning-based mapping approach outperforms previous simple word-level matching algorithms because it can account for contextual and semantic information. Additionally, we demonstrate that the manner in which negative training samples are selected significantly affects the overall performance of the system.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Incorporating the semantics of code descriptions more significantly increases matching accuracy compared to traditional text co-occurrence-based approaches. The negative training sample collection methodology is also an important component of the proposed trainable system that can be adopted in both present and future related systems.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocab030","type":"journal-article","created":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T20:29:35Z","timestamp":1612556975000},"page":"1489-1496","source":"Crossref","is-referenced-by-count":19,"title":["Deep-learning-based automated terminology mapping in OMOP-CDM"],"prefix":"10.1093","volume":"28","author":[{"given":"Byungkon","family":"Kang","sequence":"first","affiliation":[{"name":"Department of Computer Science, State University of New York, Incheon, South Korea"}]},{"given":"Jisang","family":"Yoon","sequence":"additional","affiliation":[{"name":"Graduate School of Information, Yonsei University, Seoul, South Korea"}]},{"given":"Ha Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Graduate School of Information, Yonsei University, Seoul, South Korea"}]},{"given":"Sung Jin","family":"Jo","sequence":"additional","affiliation":[{"name":"Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, North Gyeongsang,South Korea"}]},{"given":"Yourim","family":"Lee","sequence":"additional","affiliation":[{"name":"RWE Analytics, EvidNet, Seongnam-si, Gyeonggi-do, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1522-4539","authenticated-orcid":false,"given":"Hye Jin","family":"Kam","sequence":"additional","affiliation":[{"name":"Healthcare, Life Solution Cluster, New Business Unit, Hanwha Life, Seoul, South Korea"}]}],"member":"286","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"issue":"3","key":"2021071421214649800_ocab030-B1","doi-asserted-by":"crossref","first-page":"e55811","DOI":"10.1371\/journal.pone.0055811","article-title":"SHRINE: enabling nationally scalable multi-site disease Studies","volume":"8","author":"McMurry","year":"2013","journal-title":"PLoS ONE"},{"key":"2021071421214649800_ocab030-B2","first-page":"2020: 71","article-title":"Standardizing clinical diagnoses: evaluating alternate terminology selection","author":"Burrows","year":"2020","journal-title":"AMIA Summits Transl Sci Proc"},{"key":"2021071421214649800_ocab030-B3","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1075\/hot.2.05wer","volume-title":"Handbook of Terminology","author":"Wermuth","year":"2019"},{"issue":"1","key":"2021071421214649800_ocab030-B4","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1177\/1040638717738276","article-title":"A review of medical terminology standards and structured reporting","volume":"30","author":"Awaysheh","year":"2018","journal-title":"J Vet Diagn Invest"},{"key":"2021071421214649800_ocab030-B5","author":"Luna","year":"2018"},{"issue":"10","key":"2021071421214649800_ocab030-B6","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1093\/jamia\/ocy093","article-title":"Web services for data warehouses: OMOP and PCORnet on i2b2","volume":"25","author":"Klann","year":"2018","journal-title":"J Am Med Inform Associ"},{"issue":"6","key":"2021071421214649800_ocab030-B7","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1097\/PHH.0000000000000614","article-title":"Distributed data networks that support public health information needs","volume":"23","author":"Tabano","year":"2017","journal-title":"J Public Health Manag Pract"},{"key":"2021071421214649800_ocab030-B8","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.jbi.2016.10.016","article-title":"Evaluating common data models for use with a longitudinal community registry","volume":"64","author":"Garza","year":"2016","journal-title":"Journal of Biomedical Informatics"},{"issue":"2","key":"2021071421214649800_ocab030-B9","doi-asserted-by":"crossref","first-page":"e0212463","DOI":"10.1371\/journal.pone.0212463","article-title":"Data model harmonization for the All of Us Research Program: Transforming i2b2 data into the OMOP common data model","volume":"14","author":"Klann","year":"2019","journal-title":"PloS One"},{"issue":"4","key":"2021071421214649800_ocab030-B10","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1002\/cpt.1785","article-title":"Can we rely on results from IQVIA medical research data UK converted to the observational medical outcome partnership common data model? 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