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dc.contributor.advisorPeter Szolovits.en_US
dc.contributor.authorNakrin, Andrew S. (Andrew Steven), 1952-en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2005-09-26T19:17:06Z
dc.date.available2005-09-26T19:17:06Z
dc.date.copyright2001en_US
dc.date.issued2001en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/28234
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.en_US
dc.descriptionIncludes bibliographical references (p. 113-117).en_US
dc.description.abstractTagMeds is a system that recognizes and marks textual descriptions of a patient's current medications in the unstructured textual content of consultations letters. Medications are found based on their names and on linguistic patterns describing their dose, form of administration, etc. The UMLS is used as the underlying database of terms, and detected medications are encoded into XML tags consistent with and making use of the Health Level 7 (HL7) Clinical Document Architecture. The specific aims of this research are: (1) to review the literature in order to determine the state of the art in tagging free text for search and utilization, (2) to construct a tool that will reliably generate UMLS Concept Unique Identifier tags of current medications within free text. The methods involved are: (1) creating Perl procedures to recognize patterns in free text to retrieve the UMLS Concept Unique Identifiers and to insert these unique identifiers into XML tagging of the text and (2) statistical analysis of the use of TagMeds on a data base of consultation letters from the Endocrinology Clinic of the Children's Hospital of Boston as compared to manual markup by a group of physicians. The performance of an NLP system is found to be at least as sensitive as the performance of physicians in the extraction of current medications and their attributes. The tagged current medication information has the potential to support a personal electronic medical record system, such as PING. Additional development of TagMeds is likely to bring significant improvements, with modest expenditure of time and effort. TagMeds demonstrates that great utility can be achieved with a medical natural language processing system using simple and unsophisticated techniques.en_US
dc.description.statementofresponsibilityby Andrew S. Nakrin.en_US
dc.format.extent117 p.en_US
dc.format.extent5795496 bytes
dc.format.extent5810211 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleTagMeds : a tool for populating eXtensible Markup Language documents with UMLS concept unique identifiers of current medicationsen_US
dc.title.alternativeTool for populating eXtensible Markup Language documents with UMLS concept unique identifiers of current medicationsen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc49235996en_US


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