{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T08:19:24Z","timestamp":1772525964036,"version":"3.50.1"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T00:00:00Z","timestamp":1599696000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"National Institute of General Medical Studies","award":["R01 GM114355"],"award-info":[{"award-number":["R01 GM114355"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>In Hebrew online health communities, participants commonly write medical terms that appear as transliterated forms of a source term in English. Such transliterations introduce high variability in text and challenge text-analytics methods. To reduce their variability, medical terms must be normalized, such as linking them to Unified Medical Language System (UMLS) concepts. We present a method to identify both transliterated and translated Hebrew medical terms and link them with UMLS entities.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>We investigate the effect of linking terms in Camoni, a popular Israeli online health community in Hebrew. Our method, MDTEL (Medical Deep Transliteration Entity Linking), includes (1) an attention-based recurrent neural network encoder-decoder to transliterate words and mapping UMLS from English to Hebrew, (2) an unsupervised method for creating a transliteration dataset in any language without manually labeled data, and (3) an efficient way to identify and link medical entities in the Hebrew corpus to UMLS concepts, by producing a high-recall list of candidate medical terms in the corpus, and then filtering the candidates to relevant medical terms.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We carry out experiments on 3 disease-specific communities: diabetes, multiple sclerosis, and depression. MDTEL tagging and normalizing on Camoni posts achieved 99% accuracy, 92% recall, and 87% precision. When tagging and normalizing terms in queries from the Camoni search logs, UMLS-normalized queries improved search results in 46% of the cases.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Cross-lingual UMLS entity linking from Hebrew is possible and improves search performance across communities. Annotated datasets, annotation guidelines, and code are made available online (https:\/\/github.com\/yonatanbitton\/mdtel).<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocaa150","type":"journal-article","created":{"date-parts":[[2020,7,2]],"date-time":"2020-07-02T11:08:49Z","timestamp":1593688129000},"page":"1585-1592","source":"Crossref","is-referenced-by-count":9,"title":["Cross-lingual Unified Medical Language System entity linking in online health communities"],"prefix":"10.1093","volume":"27","author":[{"given":"Yonatan","family":"Bitton","sequence":"first","affiliation":[{"name":"Department of Computer Science, Ben Gurion University, Beer Sheva, Israel"}]},{"given":"Raphael","family":"Cohen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Ben Gurion University, Beer Sheva, Israel"}]},{"given":"Tamar","family":"Schifter","sequence":"additional","affiliation":[{"name":"Gertner Institute for Epidemiology and Health Policy Research, Tel HaShomer, Israel"}]},{"given":"Eitan","family":"Bachmat","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Ben Gurion University, Beer Sheva, Israel"}]},{"given":"Michael","family":"Elhadad","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Ben Gurion University, Beer Sheva, Israel"}]},{"given":"No\u00e9mie","family":"Elhadad","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,9,10]]},"reference":[{"key":"2020110613120378100_ocaa150-B1","first-page":"140","author":"Civan","year":"2007"},{"key":"2020110613120378100_ocaa150-B2","first-page":"3217","author":"Mamykina","year":"2015"},{"issue":"2","key":"2020110613120378100_ocaa150-B3","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1093\/jamia\/ocw093","article-title":"Online cancer communities as informatics intervention for 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