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Identification of concepts from emer...

Travers, Debbie.

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Identification of concepts from emergency department text using natural language processing techniques and the Unified Medical Language SystemRTM.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identification of concepts from emergency department text using natural language processing techniques and the Unified Medical Language SystemRTM./
Author:	Travers, Debbie.
Description:	224 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-11, Section: A, page: 3885.
Contained By:	Dissertation Abstracts International64-11A.
Subject:	Information Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3112086
ISBN:	0496596160

Identification of concepts from emergency department text using natural language processing techniques and the Unified Medical Language SystemRTM.
Travers, Debbie.

Identification of concepts from emergency department text using natural language processing techniques and the Unified Medical Language SystemRTM. - 224 p.

Source: Dissertation Abstracts International, Volume: 64-11, Section: A, page: 3885.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2003.

This research is part of a larger project to develop a thesaurus for emergency department (ED) chief complaint (CC) information. The CC is the patient's reason for visiting the ED, and is determined by the triage nurse in the initial minutes of the visit. The nature and severity of the CC directly influence many aspects of the patient's ED visit. CC data are also vital for public health surveillance activities. Despite the significance of the CC, there is no standard terminology for CC.

ISBN: 0496596160Subjects--Topical Terms:

1017528
Information Science.

Identification of concepts from emergency department text using natural language processing techniques and the Unified Medical Language SystemRTM.
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245 1 0 $a Identification of concepts from emergency department text using natural language processing techniques and the Unified Medical Language SystemRTM.
300 $a 224 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-11, Section: A, page: 3885.
500 $a Director: Stephanie W. Haas.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2003.
520 $a This research is part of a larger project to develop a thesaurus for emergency department (ED) chief complaint (CC) information. The CC is the patient's reason for visiting the ED, and is determined by the triage nurse in the initial minutes of the visit. The nature and severity of the CC directly influence many aspects of the patient's ED visit. CC data are also vital for public health surveillance activities. Despite the significance of the CC, there is no standard terminology for CC.
520 $a The goals of this research were to identify the concepts that comprise the domain of ED CC, and to develop a modular natural language processing (NLP) system for use in processing clinical text. The resulting Emergency Medical Text Processor (EMT-P) system is a series of modules that extracts standardized terms from clinical text using NLP and the Unified Medical Language SystemRTM. After applying EMT-P to a corpus of CC data representing all visits to three EDs during a one-year period of time, 83% of the original CC entries matched a UMLS concept. Samples of text/UMLS concept matches and non-matches were evaluated to determine the accuracy of EMT-P. 96% of the matches were rated equivalent or related, and 38% of the non-matches were found to match UMLS concepts. The results show that EMT-P Version 1 is relatively accurate; areas needing improvement in future versions of EMT-P were identified.
520 $a In the course of this study, a modular NLP system called EMT-P was developed and used to process a corpus of clinical text and extract standardized terms from the majority of entries. 3898 ED CC concepts were identified for possible inclusion in an ED CC Thesaurus, and produced a model of the domain of ED CC. A set of recommendations for developing the ED CC Thesaurus was also compiled, and included further validation of EMT-P, some specific areas of content to be included, the need to address CC-related data, and operational issues regarding design and implementation. Future plans include application of EMT-P to other types of clinical text, including triage nurses' notes, and clinical reports.
590 $a School code: 0153.
650 4 $a Information Science. $3 1017528
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710 2 0 $a The University of North Carolina at Chapel Hill. $3 1017449
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790 1 0 $a Haas, Stephanie W., $e advisor
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791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3112086