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Ozlem Uzuner and Peter Szolovits. Sibanda, Tawanda Carleton Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2007-04-03T17:11:13Z 2007-04-03T17:11:13Z 2006 2006 http://hdl.handle.net/1721.1/37097 84844278 Includes bibliographical references (leaves 103-107). Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. In this thesis, we detail an approach to extracting key information in medical discharge summaries. Starting with a narrative patient report, we first identify and remove information that compromises privacy (de-identification); next we recognize words and phrases in the text belonging to semantic categories of interest to doctors (semantic category recognition). For disease and symptoms, we determine whether the problem is present, absent, uncertain, or associated with somebody else (assertion classification). Finally, we classify the semantic relationships existing between our categories (semantic relationship classification). Our approach utilizes a series of statistical models that rely heavily on local lexical and syntactic context, and achieve competitive results compared to more complex NLP solutions. We conclude the thesis by presenting the design for the Category and Relationship Extractor (CaRE). CaRE combines our solutions to de-identification, semantic category recognition, assertion classification, and semantic relationship classification into a single application that facilitates the easy extraction of semantic information from medical text. by Tawanda Carleton Sibanda. M.Eng. 107 leaves eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Was the patient cured? : understanding semantic categories and their relationship in patient records Semantic interpretation of medical discharge summaries Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="2d5e0727-2441-47fa-b380-60844c7d18ef"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Was the patient cured? : understanding semantic categories and their relationship in patient records</Title> <Subtitle>Semantic interpretation of medical discharge summaries</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2006</PublicationDate> <Authors> <Author> <DisplayName>Sibanda, Tawanda Carleton</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>In this thesis, we detail an approach to extracting key information in medical discharge summaries. Starting with a narrative patient report, we first identify and remove information that compromises privacy (de-identification); next we recognize words and phrases in the text belonging to semantic categories of interest to doctors (semantic category recognition). For disease and symptoms, we determine whether the problem is present, absent, uncertain, or associated with somebody else (assertion classification). Finally, we classify the semantic relationships existing between our categories (semantic relationship classification). Our approach utilizes a series of statistical models that rely heavily on local lexical and syntactic context, and achieve competitive results compared to more complex NLP solutions. We conclude the thesis by presenting the design for the Category and Relationship Extractor (CaRE). CaRE combines our solutions to de-identification, semantic category recognition, assertion classification, and semantic relationship classification into a single application that facilitates the easy extraction of semantic information from medical text.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>