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Was the Patient Cured? Understanding Semantic Categories and Their Relationships in Patient Records

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dc.contributor.advisor Peter Szolovits
dc.contributor.author Sibanda, Tawanda Carleton
dc.contributor.other Clinical Decision-Making
dc.date.accessioned 2006-06-29T13:22:10Z
dc.date.available 2006-06-29T13:22:10Z
dc.date.issued 2006-06-28
dc.identifier.other MIT-CSAIL-TR-2006-048
dc.identifier.uri http://hdl.handle.net/1721.1/33223
dc.description MEng thesis
dc.description.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 complexNLP 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 singleapplication that facilitates the easy extraction of semantic information from medical text.
dc.format.extent 108 p.
dc.format.extent 2366072 bytes
dc.format.extent 9070788 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/postscript
dc.language.iso en_US
dc.relation.ispartofseries Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
dc.subject medical language processing
dc.subject MLP
dc.subject discharge summary
dc.title Was the Patient Cured? Understanding Semantic Categories and Their Relationships in Patient Records


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