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Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents

Textractor: a hybrid system for medications and reason for their prescription extraction from... Objective: To describe a new medication information extraction system—Textractor—developed for the ‘i2b2 medication extraction challenge’. The development, functionalities, and official evaluation of the system are detailed.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 F1-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 F1-measure, reached about 77% for exact matches, with a recall of 72% and a precision of 83%. Performance was the best with route information (F1-measure about 86%), and was good for dosage and frequency information, with F1-measures of about 82–85%. Results were not as good for durations, with F1-measures of 36–39%, and for reasons, with F1-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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Medical Informatics Association Oxford University Press

Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents

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References (18)

Publisher
Oxford University Press
Copyright
© 2010, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
ISSN
1067-5027
eISSN
1527-974X
DOI
10.1136/jamia.2010.004028
pmid
20819864
Publisher site
See Article on Publisher Site

Abstract

Objective: To describe a new medication information extraction system—Textractor—developed for the ‘i2b2 medication extraction challenge’. The development, functionalities, and official evaluation of the system are detailed.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 F1-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 F1-measure, reached about 77% for exact matches, with a recall of 72% and a precision of 83%. Performance was the best with route information (F1-measure about 86%), and was good for dosage and frequency information, with F1-measures of about 82–85%. Results were not as good for durations, with F1-measures of 36–39%, and for reasons, with F1-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.

Journal

Journal of the American Medical Informatics AssociationOxford University Press

Published: Sep 1, 2010

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