| Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents. | |
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MedLine Citation:
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PMID: 20819864 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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. |
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Authors:
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Stéphane M Meystre; Julien Thibault; Shuying Shen; John F Hurdle; Brett R South |
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Publication Detail:
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Type: Journal Article; Research Support, N.I.H., Extramural |
Journal Detail:
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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:
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Created Date: 2010-09-07 Completed Date: 2010-11-15 Revised Date: 2011-12-05 |
Medline Journal Info:
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Nlm Unique ID: 9430800 Medline TA: J Am Med Inform Assoc Country: United States |
Other Details:
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Languages: eng Pagination: 559-62 Citation Subset: IM |
Affiliation:
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Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA. stephane.meystre@hsc.utah.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Artificial Intelligence Electronic Health Records* Humans Information Storage and Retrieval / methods* Natural Language Processing* Pattern Recognition, Automated Pharmaceutical Preparations* |
| Grant Support | |
ID/Acronym/Agency:
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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:
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0/Pharmaceutical Preparations |
| Comments/Corrections | |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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