| Automatic extraction of medication information from medical discharge summaries. | |
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MedLine Citation:
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PMID: 20819861 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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OBJECTIVE: This article describes a system developed for the 2009 i2b2 Medication Extraction Challenge. The purpose of this challenge is to extract medication information from hospital discharge summaries. DESIGN: The system explored several linguistic natural language processing techniques (eg, term-based and token-based rule matching) to identify medication-related information in the narrative text. A number of lexical resources was constructed to profile lexical or morphological features for different categories of medication constituents. MEASUREMENTS: Performance was evaluated in terms of the micro-averaged F-measure at the horizontal system level. RESULTS: The automated system performed well, and achieved an F-micro of 80% for the term-level results and 81% for the token-level results, placing it sixth in exact matches and fourth in inexact matches in the i2b2 competition. CONCLUSION: The overall results show that this relatively simple rule-based approach is capable of tackling multiple entity identification tasks such as medication extraction under situations in which few training documents are annotated for machine learning approaches, and the entity information can be characterized with a set of feature tokens. |
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Authors:
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Hui Yang |
Publication Detail:
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Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't |
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: 2011-09-13 |
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: 545-8 Citation Subset: IM |
Affiliation:
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Department of Computing, Open University, Milton Keynes, UK. h.yang@open.ac.uk |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Electronic Health Records* Humans Information Storage and Retrieval / methods* Natural Language Processing* Patient Discharge* Pharmaceutical Preparations* Software Design |
| Grant Support | |
ID/Acronym/Agency:
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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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