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Existing models for determining modifiers of clinical entities involve regular expression or features weights that are trained independently for each modifier.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We develop and evaluate a multi-task transformer architecture design where modifiers are learned and predicted jointly using the publicly available SemEval 2015 Task 14 corpus and a new Opioid Use Disorder (OUD) data set that contains modifiers shared with SemEval as well as novel modifiers specific for OUD. We evaluate the effectiveness of our multi-task learning approach versus previously published systems and assess the feasibility of transfer learning for clinical entity modifiers when only a portion of clinical modifiers are shared.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Our approach achieved state-of-the-art results on the ShARe corpus from SemEval 2015 Task 14, showing an increase of 1.1% on weighted accuracy, 1.7% on unweighted accuracy, and 10% on micro F1 scores.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>We show that learned weights from our shared model can be effectively transferred to a new partially matched data set, validating the use of transfer learning for clinical text modifiers.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13326-024-00311-4","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T06:02:07Z","timestamp":1717740127000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multi-task transfer learning for the prediction of entity modifiers in clinical text: application to opioid use disorder case detection"],"prefix":"10.1186","volume":"15","author":[{"given":"Abdullateef I.","family":"Almudaifer","sequence":"first","affiliation":[]},{"given":"Whitney","family":"Covington","sequence":"additional","affiliation":[]},{"given":"JaMor","family":"Hairston","sequence":"additional","affiliation":[]},{"given":"Zachary","family":"Deitch","sequence":"additional","affiliation":[]},{"given":"Ankit","family":"Anand","sequence":"additional","affiliation":[]},{"given":"Caleb M.","family":"Carroll","sequence":"additional","affiliation":[]},{"given":"Estera","family":"Crisan","sequence":"additional","affiliation":[]},{"given":"William","family":"Bradford","sequence":"additional","affiliation":[]},{"given":"Lauren A.","family":"Walter","sequence":"additional","affiliation":[]},{"given":"Ellen F.","family":"Eaton","sequence":"additional","affiliation":[]},{"given":"Sue S.","family":"Feldman","sequence":"additional","affiliation":[]},{"given":"John D.","family":"Osborne","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"311_CR1","doi-asserted-by":"crossref","unstructured":"Zhong Z, Chen D. 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