Document Detail


Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents.
MedLine Citation:
PMID:  20819864     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
DESIGN: Textractor is based on the Apache Unstructured Information Management Architecture (UMIA) framework, and uses methods that are a hybrid between machine learning and pattern matching. Two modules in the system are based on machine learning algorithms, while other modules use regular expressions, rules, and dictionaries, and one module embeds MetaMap Transfer.
MEASUREMENTS: The official evaluation was based on a reference standard of 251 discharge summaries annotated by all teams participating in the challenge. The metrics used were recall, precision, and the F(1)-measure. They were calculated with exact and inexact matches, and were averaged at the level of systems and documents.
RESULTS: The reference metric for this challenge, the system-level overall F(1)-measure, reached about 77% for exact matches, with a recall of 72% and a precision of 83%. Performance was the best with route information (F(1)-measure about 86%), and was good for dosage and frequency information, with F(1)-measures of about 82-85%. Results were not as good for durations, with F(1)-measures of 36-39%, and for reasons, with F(1)-measures of 24-27%.
CONCLUSION: The official evaluation of Textractor for the i2b2 medication extraction challenge demonstrated satisfactory performance. This system was among the 10 best performing systems in this challenge.
Authors:
Stéphane M Meystre; Julien Thibault; Shuying Shen; John F Hurdle; Brett R South
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural    
Journal Detail:
Title:  Journal of the American Medical Informatics Association : JAMIA     Volume:  17     ISSN:  1527-974X     ISO Abbreviation:  J Am Med Inform Assoc     Publication Date:    2010 Sep-Oct
Date Detail:
Created Date:  2010-09-07     Completed Date:  2010-11-15     Revised Date:  2011-12-05    
Medline Journal Info:
Nlm Unique ID:  9430800     Medline TA:  J Am Med Inform Assoc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  559-62     Citation Subset:  IM    
Affiliation:
Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA. stephane.meystre@hsc.utah.edu
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MeSH Terms
Descriptor/Qualifier:
Artificial Intelligence
Electronic Health Records*
Humans
Information Storage and Retrieval / methods*
Natural Language Processing*
Pattern Recognition, Automated
Pharmaceutical Preparations*
Grant Support
ID/Acronym/Agency:
R21 LM009967/LM/NLM NIH HHS; R21 LM009967-01/LM/NLM NIH HHS; R21 LM009967-01S1/LM/NLM NIH HHS; R21 LM009967-02/LM/NLM NIH HHS; U54LM008748/LM/NLM NIH HHS
Chemical
Reg. No./Substance:
0/Pharmaceutical Preparations
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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