Treffer: A normalized lexical lookup approach to identifying UMLS concepts in free text.

Title:
A normalized lexical lookup approach to identifying UMLS concepts in free text.
Authors:
Bashyam V; Medical Imaging Informatics Group, University of California, Los Angeles, United States. vbashyam@ucla.edu, Divita G, Bennett DB, Browne AC, Taira RK
Source:
Studies in health technology and informatics [Stud Health Technol Inform] 2007; Vol. 129 (Pt 1), pp. 545-9.
Publication Type:
Journal Article; Research Support, N.I.H., Extramural
Language:
English
Journal Info:
Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Print ISSN: 0926-9630 (Print) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform
Imprint Name(s):
Original Publication: Amsterdam ; Washington, DC : IOS Press, 1991-
MeSH Terms:
Natural Language Processing* , Unified Medical Language System*, Information Storage and Retrieval/*methods, Humans ; Medical Records Systems, Computerized ; Neurology ; Radiology Department, Hospital ; Radiology Information Systems
Entry Date(s):
Date Created: 20071004 Date Completed: 20071128 Latest Revision: 20080710
Update Code:
20250114
PMID:
17911776
Database:
MEDLINE

Weitere Informationen

The National Library of Medicine has developed a tool to identify medical concepts from the Unified Medical Language System in free text. This tool - MetaMap (and its java version MMTx) has been used extensively for biomedical text mining applications. We have developed a module for MetaMap which has a high performance in terms of processing speed. We evaluated our module independently against MetaMap for the task of identifying UMLS concepts in free text clinical radiology reports. A set of 1000 sentences from neuro-radiology reports were collected and processed using our technique and the MMTx Program. An evaluation showed that our technique was able to identify 91% of the concepts found by MMTx in 14% of the time taken by MMTx. An error analysis showed that the missing concepts were largely those which were not direct lexical matches but inferential matches of multiple concepts. Our method also identified multi-phrase concepts which MMTx failed to identify. We suggest that this module be implemented as an option in MMTx for real-time text mining applications where single concepts found in the UMLS need to be identified.