Was the patient cured? : understanding semantic categories and their relationship in patient records

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Sibanda, Tawanda Carleton

Advisor dc:contributor.advisor

• Ozlem Uzuner and Peter Szolovits.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/37097

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/37097