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Machine Learning and Rule-based Approaches to Assertion Classification

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dc.contributor.author Uzuner, Ozlem
dc.contributor.author Zhang, Xiaoran
dc.contributor.author Sibanda, Tawanda
dc.date.accessioned 2010-03-09T21:43:46Z
dc.date.available 2010-03-09T21:43:46Z
dc.date.issued 2009
dc.date.submitted 2008-08
dc.identifier.issn 1527-974X
dc.identifier.uri http://hdl.handle.net/1721.1/52450
dc.description.abstract Objectives The authors study two approaches to assertion classification. One of these approaches, Extended NegEx (ENegEx), extends the rule-based NegEx algorithm to cover alter-association assertions; the other, Statistical Assertion Classifier (StAC), presents a machine learning solution to assertion classification. Design For each mention of each medical problem, both approaches determine whether the problem, as asserted by the context of that mention, is present, absent, or uncertain in the patient, or associated with someone other than the patient. The authors use these two systems to (1) extend negation and uncertainty extraction to recognition of alter-association assertions, (2) determine the contribution of lexical and syntactic context to assertion classification, and (3) test if a machine learning approach to assertion classification can be as generally applicable and useful as its rule-based counterparts. Measurements The authors evaluated assertion classification approaches with precision, recall, and F-measure. Results The ENegEx algorithm is a general algorithm that can be directly applied to new corpora. Despite being based on machine learning, StAC can also be applied out-of-the-box to new corpora and achieve similar generality. Conclusion The StAC models that are developed on discharge summaries can be successfully applied to radiology reports. These models benefit the most from words found in the ± 4 word window of the target and can outperform ENegEx. en
dc.language.iso en_US
dc.publisher BMJ Publishing Group en
dc.relation.isversionof http://dx.doi.org/10.1197/jamia.M2950 en
dc.rights Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. en
dc.source BMJ Publishing Group en
dc.title Machine Learning and Rule-based Approaches to Assertion Classification en
dc.type Article en
dc.identifier.citation Uzuner, Özlem, Xiaoran Zhang, and Tawanda Sibanda. “Machine Learning and Rule-based Approaches to Assertion Classification.” Journal of the American Medical Informatics Association 16.1 (2009): 109-115. © 2009, British Medical Journal Publishing Group en
dc.contributor.department Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory en_US
dc.contributor.approver Uzuner, Ozlem
dc.contributor.mitauthor Uzuner, Ozlem
dc.contributor.mitauthor Zhang, Xiaoran
dc.contributor.mitauthor Sibanda, Tawanda
dc.relation.journal Journal of the American Medical Informatics Association en
dc.identifier.mitlicense PUBLISHER_POLICY en
dc.eprint.version Final published version en
dc.identifier.pmid 18952931
dc.type.uri http://purl.org/eprint/type/JournalArticle en
eprint.status http://purl.org/eprint/status/PeerReviewed en
dspace.orderedauthors Uzuner, O.; Zhang, X.; Sibanda, T. en


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