{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T05:24:59Z","timestamp":1771305899028,"version":"3.50.1"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"e1","funder":[{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"crossref","award":["R01LM010681"],"award-info":[{"award-number":["R01LM010681"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"crossref","award":["2R01LM010681-05"],"award-info":[{"award-number":["2R01LM010681-05"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"crossref","award":["1R01GM103859"],"award-info":[{"award-number":["1R01GM103859"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"crossref","award":["1R01GM102282"],"award-info":[{"award-number":["1R01GM102282"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,4,1]]},"abstract":"<jats:p>Objective: The goal of this study was to develop a practical framework for recognizing and disambiguating clinical abbreviations, thereby improving current clinical natural language processing (NLP) systems\u2019 capability to handle abbreviations in clinical narratives.<\/jats:p><jats:p>Methods: We developed an open-source framework for clinical abbreviation recognition and disambiguation (CARD) that leverages our previously developed methods, including: (1) machine learning based approaches to recognize abbreviations from a clinical corpus, (2) clustering-based semiautomated methods to generate possible senses of abbreviations, and (3) profile-based word sense disambiguation methods for clinical abbreviations. We applied CARD to clinical corpora from Vanderbilt University Medical Center (VUMC) and generated 2 comprehensive sense inventories for abbreviations in discharge summaries and clinic visit notes. Furthermore, we developed a wrapper that integrates CARD with MetaMap, a widely used general clinical NLP system.<\/jats:p><jats:p>Results and Conclusion: CARD detected 27\u2009317 and 107\u2009303 distinct abbreviations from discharge summaries and clinic visit notes, respectively. Two sense inventories were constructed for the 1000 most frequent abbreviations in these 2 corpora. Using the sense inventories created from discharge summaries, CARD achieved an F1 score of 0.755 for identifying and disambiguating all abbreviations in a corpus from the VUMC discharge summaries, which is superior to MetaMap and Apache\u2019s clinical Text Analysis Knowledge Extraction System (cTAKES). Using additional external corpora, we also demonstrated that the MetaMap-CARD wrapper improved MetaMap\u2019s performance in recognizing disorder entities in clinical notes. The CARD framework, 2 sense inventories, and the wrapper for MetaMap are publicly available at https:\/\/sbmi.uth.edu\/ccb\/resources\/abbreviation.htm. We believe the CARD framework can be a valuable resource for improving abbreviation identification in clinical NLP systems.<\/jats:p>","DOI":"10.1093\/jamia\/ocw109","type":"journal-article","created":{"date-parts":[[2016,8,19]],"date-time":"2016-08-19T01:50:09Z","timestamp":1471571409000},"page":"e79-e86","source":"Crossref","is-referenced-by-count":50,"title":["A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD)"],"prefix":"10.1093","volume":"24","author":[{"given":"Yonghui","family":"Wu","sequence":"first","affiliation":[{"name":"School of Biomedical Informatics, The University of Texas Health Science Center at Houston"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua C","family":"Denny","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee"},{"name":"Department of Medicine, 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