Automated proof reading of clinical notes

PACLIC 25 - Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

Volumn , Issue , 2011, Pages 303-312

  Patrick, Jon ^a   Nguyen, Dung ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Clinical; Normalization; Proof reading; Spelling; Standardization

Indexed keywords

AUTOMATED PROOFS; AUTOMATIC PROOFS; CLINICAL; CLINICAL STAFF; HIGH QUALITY INFORMATION; KNOWLEDGE DISCOVERY PROCESS; NORMALIZATION; PROOF READING; SEMANTIC PROCESSING; SPELLING; SYSTEM OUTPUT;

SEMANTICS; STANDARDIZATION;

INFORMATION DISSEMINATION;

EID: 84863870498     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (22)

1. Abney, S. 1996. Partial parsing via finite-state cascades. Journal of Natural Language Engineering, 2(4), 337-344.
2. Aït-Mokhtar S. and J.P. Chanod. 1997. Incremental finite-state parsing. In Proceedings of the fifth conference on Applied natural language processing (ANLC). Association for Computational Linguistics, pp. 72-79.
3. Atkinson, K. 2006. Gnu aspell 0.60.4, http://aspell.sourceforge.net/.
4. Crowell, J., Q. Zeng, L. Ngo and E.M. Lacroix. 2004. A Frequency-based technique to improve the spelling suggestion rank in medical queries. Journal of the American Medical Informatics Association, 11:179-185.
5. Divita, G. 2003. SPECIALIST Spelling Suggestion Tools (Gspell), produced by National Library of Medicine. Available at http://lexsrv3.nlm.nih.gov/LexSysGroup/Projects/gSpell/current/GSpell.html
6. Hsu C.N. and M.T. Dung. 1999. Generating finite-state transducers for semi-structured data extraction from the Web. Journal of Information Systems, 23(8), 521-538.
7. Koskenniemi, K. 1990. Finite-state parsing and disambiguation. Proceedings of the 13th conference on Computational linguistics, 2, 229-232.
8. Kuenning, G. 2006. International ispell 3.3.02. Available at http://lasr.cs.ucla.edu/geoff/ispell.html.
9. Kukich, K. 1992. Technique for automatically correcting words in text. Association for Computing Machinery (ACM) Computing Surveys, 24(4), 377-439.
10. Levenshtein, V. 1966. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 10(8), 707-710.
11. Mohri, M. 1997. Finite-state transducers in language and speech processing. Journal of Computational Linguistics, 23(2), 269-311.
12. Padro, M. and L. Padro. 2007. ME-CSSR: an extension of CSSR using maximum entropy. Proceeding of the Conference on Finite-State Methods for Natural Language Processing (FSMNLP), pp. 161-165.
13. Pakhomov S., T. Pedersen and C.G. Chute. 2005. Abbreviation and Acronym Disambiguation in Clinical Discourse. Proceedings of the Annual Symposium on Biomedical and Health Infomatics (AIMA), 2005:589-593.
14. Pakhomov S. 2002. Semi-Supervised Maximum Entropy Based Approach to Acronym and Abbreviation Normalization in Medical Texts. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (ACL), pp. 160-167.
15. Patrick J. and M. Sabbagh. 2011. An Active Learning Process for Extraction and Standardization of Medical Measurements by a Trainable FSA. In Tokyo-Japan: Springer Berlin / Heidelberg, pp. 151-162.
16. Patrick J., M. Sabbagh, S. Jain and H. Zheng. 2010. Spelling correction in Clinical Notes with Emphasis on First Suggestion Accuracy. 2nd Workshop on Building and Evaluating Resources for Biomedical Text Mining, pp. 2-8.
17. Patrick J., Y. Wang and P. Budd. 2007. An automated system for conversion of clinical notes into SNOMED clinical terminology. Proceedings of the 5th Australasian Symposium on Australasian Computer Science Week (ACSW) frontiers, 68:219-226.
18. Philips, L. 1990. Hanging on the metaphone. Computer Language Magazine, 7(12), 38-44.
19. Roche E. and Y. Schabes. 1995. Deterministic part-of-speech tagging with finite-state transducers. Journal of Computational Linguistics, 21(2), 227-253.
20. Ruch, P., R. Bauda and A. Geissbuhlera. 2003. Using lexical disambiguation and named-entity recognition to improve spelling correction in the electronic patient record. Artificial Intelligence in Medicine, 29(2003), 169-184.
21. Senger C., J. Kaltschmidt, S.P.W. Schmitt, M.G. Pruszydlo and W.E. Haefeli. 2010. Misspellings in drug information system queries: Characteristics of drug name spelling errors and strategies for their prevention. International Journal of Medical Informatics, 79(2010), 832-839.
22. Turchin, A., J.T. Chu, M. Shubina and J.S. Einbinder. 2007. Identification of misspelled words without a comprehensive dictionary using prevalence analysis. Proceedings of the Annual Symposium on Biomedical and Health Infomatics (AIMA), 11:751-755.