{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T12:08:15Z","timestamp":1710331695746},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2015,12,1]],"date-time":"2015-12-01T00:00:00Z","timestamp":1448928000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2016,12,15]],"date-time":"2016-12-15T00:00:00Z","timestamp":1481760000000},"content-version":"vor","delay-in-days":380,"URL":"https:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["NLM 2R01LM010681-05"]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biomedical Informatics"],"published-print":{"date-parts":[[2015,12]]},"DOI":"10.1016\/j.jbi.2015.09.010","type":"journal-article","created":{"date-parts":[[2015,9,15]],"date-time":"2015-09-15T17:45:41Z","timestamp":1442339141000},"page":"11-18","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":84,"title":["A study of active learning methods for named entity recognition in clinical text"],"prefix":"10.1016","volume":"58","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-3639-5847","authenticated-orcid":false,"given":"Yukun","family":"Chen","sequence":"first","affiliation":[]},{"given":"Thomas A.","family":"Lasko","sequence":"additional","affiliation":[]},{"given":"Qiaozhu","family":"Mei","sequence":"additional","affiliation":[]},{"given":"Joshua C.","family":"Denny","sequence":"additional","affiliation":[]},{"given":"Hua","family":"Xu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.jbi.2015.09.010_b0005","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1038\/gim.2013.72","article-title":"The electronic medical records and genomics (eMERGE) network: past, present, and future","volume":"15","author":"Gottesman","year":"2013","journal-title":"Genet. Med."},{"key":"10.1016\/j.jbi.2015.09.010_b0010","first-page":"1564","article-title":"Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases","volume":"2011","author":"Xu","year":"2011","journal-title":"AMIA Annu. Symp. Proc."},{"key":"10.1016\/j.jbi.2015.09.010_b0015","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1016\/j.jbi.2009.08.007","article-title":"What can natural language processing do for clinical decision support?","volume":"42","author":"Demner-Fushman","year":"2009","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.jbi.2015.09.010_b0020","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1136\/amiajnl-2014-002649","article-title":"Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality","volume":"22","author":"Xu","year":"2015","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0025","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1136\/jamia.2010.003947","article-title":"Extracting medication information from clinical text","volume":"17","author":"Uzuner","year":"2010","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0030","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1136\/amiajnl-2011-000203","article-title":"2010 i2b2\/VA challenge on concepts, assertions, and relations in clinical text","volume":"18","author":"Uzuner","year":"2011","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0035","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1136\/amiajnl-2013-001628","article-title":"Evaluating temporal relations in clinical text: 2012 i2b2 Challenge","volume":"20","author":"Sun","year":"2013","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0040","unstructured":"NIH, Unified Medical Language System (UMLS). ."},{"key":"10.1016\/j.jbi.2015.09.010_b0045","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1136\/jamia.2009.001560","article-title":"Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications","volume":"17","author":"Savova","year":"2010","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0050","doi-asserted-by":"crossref","first-page":"681","DOI":"10.7326\/0003-4819-122-9-199505010-00007","article-title":"Unlocking clinical data from narrative reports: a study of natural language processing","volume":"122","author":"Hripcsak","year":"1995","journal-title":"Ann. Intern. Med."},{"key":"10.1016\/j.jbi.2015.09.010_b0055","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1136\/jamia.2009.002733","article-title":"An overview of MetaMap: historical perspective and recent advances","volume":"17","author":"Aronson","year":"2010","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0060","first-page":"156","article-title":"Development and evaluation of a clinical note section header terminology","author":"Denny","year":"2008","journal-title":"AMIA Annu. Symp. Proc."},{"key":"10.1016\/j.jbi.2015.09.010_b0065","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1136\/jamia.2010.003939","article-title":"High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge","volume":"17","author":"Patrick","year":"2010","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0070","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1136\/amiajnl-2011-000150","article-title":"Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010","volume":"18","author":"de Bruijn","year":"2011","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0075","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1136\/amiajnl-2011-000163","article-title":"A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries","volume":"18","author":"Jiang","year":"2011","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0080","doi-asserted-by":"crossref","unstructured":"D.D. Lewis, W.A. Gale, A sequential algorithm for training text classifiers, in: Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, 1994, pp. 3\u201312.","DOI":"10.1007\/978-1-4471-2099-5_1"},{"key":"10.1016\/j.jbi.2015.09.010_b0085","unstructured":"J. Zhu, E. Hovy, Active learning for word sense disambiguation with methods for addressing the class imbalance problem, in: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2007, pp. 783\u2013790."},{"key":"10.1016\/j.jbi.2015.09.010_b0090","first-page":"45","article-title":"Support vector machine active learning with applications to text classification","volume":"2","author":"Tong","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.jbi.2015.09.010_b0095","doi-asserted-by":"crossref","unstructured":"B. Settles, M. Craven, An analysis of active learning strategies for sequence labeling tasks, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2008, pp. 1069\u20131078.","DOI":"10.3115\/1613715.1613855"},{"key":"10.1016\/j.jbi.2015.09.010_b0100","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1136\/amiajnl-2011-000648","article-title":"Active learning for clinical text classification: is it better than random sampling?","volume":"19","author":"Figueroa","year":"2012","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0105","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.jbi.2011.11.003","article-title":"Applying active learning to assertion classification of concepts in clinical text","volume":"45","author":"Chen","year":"2012","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.jbi.2015.09.010_b0110","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1136\/amiajnl-2012-001244","article-title":"Applying active learning to supervised word sense disambiguation in MEDLINE","volume":"20","author":"Chen","year":"2013","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0115","doi-asserted-by":"crossref","first-page":"e253","DOI":"10.1136\/amiajnl-2013-001945","article-title":"Applying active learning to high-throughput phenotyping algorithms for electronic health records data","volume":"20","author":"Chen","year":"2013","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0120","series-title":"Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers","first-page":"69","article-title":"MMR-based active machine learning for bio named entity recognition","author":"Kim","year":"2006"},{"key":"10.1016\/j.jbi.2015.09.010_b0125","series-title":"Selective supervision: guiding supervised learning with decision-theoretic active learning","first-page":"877","author":"Kapoor","year":"2007"},{"key":"10.1016\/j.jbi.2015.09.010_b0130","series-title":"Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing","first-page":"18","article-title":"Estimating annotation cost for active learning in a multi-annotator environment","author":"Arora","year":"2009"},{"key":"10.1016\/j.jbi.2015.09.010_b0135","series-title":"Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers","first-page":"65","article-title":"Assessing the costs of sampling methods in active learning for annotation","author":"Haertel","year":"2008"},{"key":"10.1016\/j.jbi.2015.09.010_b0140","article-title":"Large scale online learning","volume":"vol. 16","author":"Bottou","year":"2004"},{"key":"10.1016\/j.jbi.2015.09.010_b0145","doi-asserted-by":"crossref","unstructured":"A.B. Goldberg, X. Zhu, A. Furger, J.-M. Xu, OASIS: Online Active Semi-Supervised Learning, 2011.","DOI":"10.1609\/aaai.v25i1.7910"},{"key":"10.1016\/j.jbi.2015.09.010_b0150","series-title":"Proceedings of the Fourth Linguistic Annotation Workshop","first-page":"56","article-title":"Influence of pre-annotation on POS-tagged corpus development","author":"Fort","year":"2010"},{"key":"10.1016\/j.jbi.2015.09.010_b0155","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1136\/amiajnl-2013-001837","article-title":"Evaluating the impact of pre-annotation on annotation speed and potential bias: natural language processing gold standard development for clinical named entity recognition in clinical trial announcements","volume":"21","author":"Lingren","year":"2014","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0160","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.jbi.2014.05.002","article-title":"Evaluating the effects of machine pre-annotation and an interactive annotation interface on manual de-identification of clinical text","volume":"50","author":"South","year":"2014","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.jbi.2015.09.010_b0165","series-title":"Proc. of the 18th International Conf. on Machine Learning","first-page":"282","article-title":"Conditional random fields: probabilistic models for segmenting and labeling sequence data","author":"Lafferty","year":"2001"},{"key":"10.1016\/j.jbi.2015.09.010_b0170","unstructured":"http:\/\/crfpp.googlecode.com\/svn\/trunk\/doc\/index.html."},{"key":"10.1016\/j.jbi.2015.09.010_b0175","unstructured":"Active Learning Challenge, 2010. ."},{"key":"10.1016\/j.jbi.2015.09.010_b0180","unstructured":"R. Socher, J. Bauer, C.D. Manning, A.Y. Ng, Parsing with Compositional Vector Grammars, ACL, 2013."},{"key":"10.1016\/j.jbi.2015.09.010_b0185","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1109\/TKDE.2006.130","article-title":"Sentence similarity based on semantic nets and corpus statistics","volume":"18","author":"Li","year":"2006","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.jbi.2015.09.010_b0190","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1197\/jamia.M1176","article-title":"\u201cUnderstanding\u201d medical school curriculum content using KnowledgeMap","volume":"10","author":"Denny","year":"2003","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.jbi.2015.09.010_b0195","unstructured":"B.T. McInnes, T. Pedersen, S.V. Pakhomov, UMLS-interface and UMLS-similarity: open source software for measuring paths and semantic similarity. In: AMIA Annu. Symp. Proc., vol. 2009, 2009, pp. 431\u2013435."},{"key":"10.1016\/j.jbi.2015.09.010_b0200","first-page":"147","article-title":"Using corpus statistics and WordNet relations for sense identification","volume":"24","author":"Leacock","year":"1998","journal-title":"Comput. Linguist."},{"key":"10.1016\/j.jbi.2015.09.010_b0205","series-title":"Proceedings of the 32nd annual meeting on Association for Computational Linguistics","first-page":"133","article-title":"Verbs semantics and lexical selection","author":"Wu","year":"1994"},{"key":"10.1016\/j.jbi.2015.09.010_b0210","unstructured":"NIH, SNOMED Clinical Terms (SNOMED CT). ."},{"key":"10.1016\/j.jbi.2015.09.010_b0215","unstructured":"NIH, MeSH. ."},{"key":"10.1016\/j.jbi.2015.09.010_b0220","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.1136800","article-title":"Clustering by passing messages between data points","volume":"315","author":"Frey","year":"2007","journal-title":"Science"},{"key":"10.1016\/j.jbi.2015.09.010_b0225","series-title":"A Maximum Entropy Approach to Named Entity Recognition","author":"Borthwick","year":"1999"},{"key":"10.1016\/j.jbi.2015.09.010_b0230","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"10.1016\/j.jbi.2015.09.010_b0235","first-page":"1453","article-title":"Large margin methods for structured and interdependent output variables","volume":"6","author":"Tsochantaridis","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.jbi.2015.09.010_b0240","doi-asserted-by":"crossref","DOI":"10.1186\/1472-6947-13-S1-S1","article-title":"Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features","author":"Tang","year":"2013","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"10.1016\/j.jbi.2015.09.010_b0245","series-title":"Proceedings of the 23rd International Conference on Computational Linguistics: Posters","first-page":"259","article-title":"Recognizing medication related entities in hospital discharge summaries using support vector machine","author":"Doan","year":"2010"},{"key":"10.1016\/j.jbi.2015.09.010_b0250","series-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","first-page":"1467","article-title":"Closing the loop: fast, interactive semi-supervised annotation with queries on features and instances","author":"Settles","year":"2011"}],"container-title":["Journal of Biomedical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046415002038?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046415002038?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T04:22:18Z","timestamp":1691986938000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1532046415002038"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,12]]},"references-count":50,"alternative-id":["S1532046415002038"],"URL":"http:\/\/dx.doi.org\/10.1016\/j.jbi.2015.09.010","relation":{},"ISSN":["1532-0464"],"issn-type":[{"value":"1532-0464","type":"print"}],"subject":["Health Informatics","Computer Science Applications"],"published":{"date-parts":[[2015,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A study of active learning methods for named entity recognition in clinical text","name":"articletitle","label":"Article Title"},{"value":"Journal of Biomedical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jbi.2015.09.010","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Copyright \u00a9 2015 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}