2026-09-19T17:13:37Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/282342022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Peter Szolovits.
Nakrin, Andrew S. (Andrew Steven), 1952-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-09-26T19:17:06Z
2005-09-26T19:17:06Z
2001
2001
http://hdl.handle.net/1721.1/28234
49235996
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (p. 113-117).
TagMeds 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.
by Andrew S. Nakrin.
S.M.
117 p.
5795496 bytes
5810211 bytes
application/pdf
application/pdf
en_US
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.
TagMeds : a tool for populating eXtensible Markup Language documents with UMLS concept unique identifiers of current medications
Tool for populating eXtensible Markup Language documents with UMLS concept unique identifiers of current medications
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<Abstract>TagMeds 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.</Abstract>
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