Treffer: Biomedical negation scope detection with conditional random fields.

Title:
Biomedical negation scope detection with conditional random fields.
Authors:
Agarwal S; Medical Informatics, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA., Yu H
Source:
Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2010 Nov-Dec; Vol. 17 (6), pp. 696-701.
Publication Type:
Evaluation Study; Journal Article; Research Support, N.I.H., Extramural
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9430800 Publication Model: Print Cited Medium: Internet ISSN: 1527-974X (Electronic) Linking ISSN: 10675027 NLM ISO Abbreviation: J Am Med Inform Assoc Subsets: MEDLINE
Imprint Name(s):
Publication: 2015- : Oxford : Oxford University Press
Original Publication: Philadelphia, PA : Hanley & Belfus, c1993-
MeSH Terms:
Natural Language Processing*, Data Mining/*methods, Humans
References:
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J Biomed Inform. 2001 Oct;34(5):301-10. (PMID: 12123149)
J Am Med Inform Assoc. 2001 Nov-Dec;8(6):598-609. (PMID: 11687566)
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BMC Med Inform Decis Mak. 2005 May 05;5:13. (PMID: 15876352)
AMIA Annu Symp Proc. 2008 Nov 06;:96-100. (PMID: 18999100)
J Biomed Inform. 2007 Jun;40(3):236-51. (PMID: 17462961)
J Am Med Inform Assoc. 2007 May-Jun;14(3):304-11. (PMID: 17329723)
Bioinformatics. 2007 Jul 1;23(13):i424-32. (PMID: 17646327)
Bioinformatics. 2003;19 Suppl 1:i340-9. (PMID: 12855479)
Grant Information:
R01 LM009836 United States LM NLM NIH HHS; R01 LM010125 United States LM NLM NIH HHS; 5R01LM009836 United States LM NLM NIH HHS; 5R01LM010125 United States LM NLM NIH HHS
Entry Date(s):
Date Created: 20101022 Date Completed: 20110218 Latest Revision: 20211020
Update Code:
20250114
PubMed Central ID:
PMC3000754
DOI:
10.1136/jamia.2010.003228
PMID:
20962133
Database:
MEDLINE

Weitere Informationen

Objective: Negation is a linguistic phenomenon that marks the absence of an entity or event. Negated events are frequently reported in both biological literature and clinical notes. Text mining applications benefit from the detection of negation and its scope. However, due to the complexity of language, identifying the scope of negation in a sentence is not a trivial task.
Design: Conditional random fields (CRF), a supervised machine-learning algorithm, were used to train models to detect negation cue phrases and their scope in both biological literature and clinical notes. The models were trained on the publicly available BioScope corpus.
Measurement: The performance of the CRF models was evaluated on identifying the negation cue phrases and their scope by calculating recall, precision and F1-score. The models were compared with four competitive baseline systems.
Results: The best CRF-based model performed statistically better than all baseline systems and NegEx, achieving an F1-score of 98% and 95% on detecting negation cue phrases and their scope in clinical notes, and an F1-score of 97% and 85% on detecting negation cue phrases and their scope in biological literature.
Conclusions: This approach is robust, as it can identify negation scope in both biological and clinical text. To benefit text mining applications, the system is publicly available as a Java API and as an online application at http://negscope.askhermes.org.