| 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. | |
