| Extracting medication information from clinical text. | |
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MedLine Citation:
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PMID: 20819854 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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The Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records focused on the identification of medications, their dosages, modes (routes) of administration, frequencies, durations, and reasons for administration in discharge summaries. This challenge is referred to as the medication challenge. For the medication challenge, i2b2 released detailed annotation guidelines along with a set of annotated discharge summaries. Twenty teams representing 23 organizations and nine countries participated in the medication challenge. The teams produced rule-based, machine learning, and hybrid systems targeted to the task. Although rule-based systems dominated the top 10, the best performing system was a hybrid. Of all medication-related fields, durations and reasons were the most difficult for all systems to detect. While medications themselves were identified with better than 0.75 F-measure by all of the top 10 systems, the best F-measure for durations and reasons were 0.525 and 0.459, respectively. State-of-the-art natural language processing systems go a long way toward extracting medication names, dosages, modes, and frequencies. However, they are limited in recognizing duration and reason fields and would benefit from future research. |
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Authors:
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Ozlem Uzuner; Imre Solti; Eithon Cadag |
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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: 2012-02-17 |
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: 514-8 Citation Subset: IM |
Affiliation:
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Department of Information Studies, University at Albany, State University of New York, Albany, NY, USA. ouzuner@albany.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Computers, Hybrid Electronic Health Records* Humans Information Storage and Retrieval / methods* Natural Language Processing* Patient Dropouts Pharmaceutical Preparations* |
| Grant Support | |
ID/Acronym/Agency:
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1K99LM010227-0110/LM/NLM NIH HHS; 2 T15 LM007442-06/LM/NLM NIH HHS; 5 U54 LM008748/LM/NLM NIH HHS; HHSN272200700057 C//PHS HHS; K99 LM010227-01/LM/NLM NIH HHS; N00244-09-1-0081//PHS HHS; R00 LM010227-02/LM/NLM NIH HHS; R00 LM010227-03/LM/NLM NIH HHS; R00 LM010227-04/LM/NLM NIH HHS; R00 LM010227-05/LM/NLM NIH HHS; T15 LM07442/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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