High throughput modularized NLP system for clinical text

ACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Volumn , Issue , 2005, Pages 25-28

  Pakhomov, Serguei ^a   Buntrock, James ^b   Duffy, Patrick ^b  

^a Mayo Clinic   (United States)

^b MAYO CLINIC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ABSTRACTORS; HIGH THROUGHPUT; KEY-PHRASE; MODULARIZED; NLP SYSTEMS; REAL TIME; TEXT ANALYSIS;

COMPUTATIONAL LINGUISTICS; NATURAL LANGUAGE PROCESSING SYSTEMS;

THROUGHPUT;

EID: 84859894114     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Friedman, C., et al., A general natural-language text processor for clinical radiology. Journal of American Medical Informatics Association, 1994. 1(2): p. 161-174.
2. Friedman, C. Towards a Comprehensive Medical Language Processing System: Methods and Issues. in American Medical Informatics Association (AMIA). 1997.
3. Aronson, A. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. in Proceedings of the 2001 AMIA Annual Symposium. 2001. Washington, DC.
4. Mitchell, K. and R. Crowley. GNegEx - Implementation and Evaluation of a Negation Tagger for the Shared Pathology Iinformatics Network. in Advancing Practice, Instruction and Innovation through Informatics (APIII). 2003.
5. Thompson-McInness, B., S. Pakhomov, and T. Pedersen. Automating Spelling Correction Tools Using Bigram Statistics. in Medinfo Symposium. 2004. San Francisco, CA, USA.
6. Coden, A., et al., Domain-specific language models and lexicons for tagging. In print in Journal of Biomedical Informatics, 2005.
7. Pakhomov, S., A. Coden, and C. Chute, Developing a Corpus of Clinical Notes Manually Annotated for Part-of-Speech. To appear in International Journal of Medical Informatics, 2005(Special Issue on Natural Language Processing in Biomedical Applications).
8. Pakhomov, S. Semi-Supervised Maximum Entropy Based Approach to Acronym and Abbreviation Normalization in Medical Texts. in 40th Meeting of the Association for Computational Linguistics (ACL 2002). 2002. Philadelohia, PA.
9. Chapman, W.W., et al. Evaluation of Negation Phrases in Narrative Clinical Reports. in American Medical Informatics Association. 2001. Washington, DC, USA.
10. Landis, J.R. and G.G. Koch, The Measurement of Observer Agreement for Categorical Data. Biometrics, 1977. 33: p. 159-174. (Pubitemid 8054849)