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dc.contributor.authorUzuner, Ozlem
dc.contributor.authorZhang, Xiaoran
dc.contributor.authorSibanda, Tawanda
dc.date.accessioned2010-03-09T21:43:46Z
dc.date.available2010-03-09T21:43:46Z
dc.date.issued2009
dc.date.submitted2008-08
dc.identifier.issn1527-974X
dc.identifier.urihttp://hdl.handle.net/1721.1/52450
dc.description.abstractObjectives 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.isoen_US
dc.publisherBMJ Publishing Groupen
dc.relation.isversionofhttp://dx.doi.org/10.1197/jamia.M2950en
dc.rightsArticle 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.sourceBMJ Publishing Groupen
dc.titleMachine Learning and Rule-based Approaches to Assertion Classificationen
dc.typeArticleen
dc.identifier.citationUzuner, 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 Groupen
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.approverUzuner, Ozlem
dc.contributor.mitauthorUzuner, Ozlem
dc.contributor.mitauthorZhang, Xiaoran
dc.contributor.mitauthorSibanda, Tawanda
dc.relation.journalJournal of the American Medical Informatics Associationen
dc.eprint.versionFinal published versionen
dc.identifier.pmid18952931
dc.type.urihttp://purl.org/eprint/type/JournalArticleen
eprint.statushttp://purl.org/eprint/status/PeerRevieweden
dspace.orderedauthorsUzuner, O.; Zhang, X.; Sibanda, T.en
dc.identifier.orcidhttps://orcid.org/0000-0001-8011-9850
mit.licensePUBLISHER_POLICYen
mit.metadata.statusComplete


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