Does negation really matter?

ABSTRACT

We explore the role negation and speculation identification plays in the multi-label document-level classification of medical reports for diseases. We identify the polarity of assertions made on noun phrases which reference diseases in the medical reports. We experiment with two machine learning classifiers: one based upon Lucene and the other based upon BoosTexter. We find the performance of these systems on document-level classification of medical reports for diseases fails to show improvement when their input is enhanced by the polarity of assertions made on noun phrases. We conclude that due to the nature of our machine learning classifiers, information on the polarity of phrase-level assertions does not improve performance on our data in a multilabel document-level classification task.

References

1. }}Kyle H. Ambert and Aaron M. Cohen. 2009. A System for Classifying Disease Comorbidity Status from Medical Discharge Summaries Using Automated Hotspot and Negated Concept Detection. Journal of the American Medical Informatics Association 16(4):590--95.
2. }}Alan R. Aronson. 2001. Effective Mapping of Biomedical Text to the UMLS Metathesaurus: The Metamap Program. Proceedings of the AMIA symposium. 17--21.
3. }}Mordechai Averbuch, Tom H. Karson, Benjamin Ben-Ami, Oded Maimon, and Lior Rokach. 2004. Context-Sensitive Medical Information Retrieval. Medinfo. MEDINFO 11(Pt 1):282--86.
4. }}Wendy W. Chapman, Will Bridewell, Paul Hanbury, Gregory F. Cooper, and Bruce G. Buchanan. 2001. A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries. Journal of Biomedical Informatics 34(5):301--10.
5. }}Thomas M. Cover and Peter E. Hart. 1967. Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory 13(1):21--27.
6. }}Marcelo Fiszman, Wendy W. Chapman, Dominik Aronsky, and R. Scott Evans. 2000. Automatic Detection of Acute Bacterial Pneumonia from Chest X-Ray Reports. Journal of the American Medical Informatics Association 7:593--604.
7. }}Ira Goldstein, Anna Arzumtsyan, and Özlem Uzuner. 2007. Three Approaches to Automatic Assignment of ICD-9-CM Codes to Radiology Reports. Proceedings of the AMIA symposium. 279--83.
8. }}Otis Gospodnetić and Erik Hatcher. 2005. Lucene in Action. Greenwich, CT: Manning.
9. }}Henk Harkema, John N. Dowling, Tyler Thornblade, and Wendy W. Chapman. 2009. Context: An Algorithm for Determining Negation, Experiencer, and Temporal Status from Clinical Reports. Journal Of Biomedical Informatics 42(5):839--51.
10. }}Jung-Jae Kim and Jong C. Park. 2006. Extracting Contrastive Information from Negation Patterns in Biomedical Literature. ACM Transactions on Asian Language Information Processing (TALIP) 5(1):44--60.
11. }}Tom Mitchell, Bruce Buchanan, Gerald DeJong, Thomas Dietterich, Paul Rosenbloom, and Alex Waibel. 1990. Machine Learning. Annual Review of Computer Science. Vol. 4. Eds. Joseph F. Traub, Barbara J. Grosz, Butler W. Lampson and Nils J. Nilsson. Palo Alto, CA: Annual Reviews.
12. }}Pradeep G. Mutalik, Aniruddha Deshpande, and Prakash M. Nadkarni. 2001. Use of General-Purpose Negation Detection to Augment Concept Indexing of Medical Documents: A Quantitative Study Using the UMLS. Journal of the American Medical Informatics Association 8(6):598--609.
13. }}National Center for Health Statistics. 2010. ICD - ICD-9-CM - International Classification of Diseases, Ninth Revision, Clinical Modification. Accessed: May 1, 2010. <www.cdc.gov/nchs/icd/icd9cm.htm>.
14. }}Arzucan Özgür, Levent Özgür, and Tunga Güngör. 2005. Text Categorization with Class-Based and Corpus-Based Keyword Selection. ISCIS 2005. Eds. Pinar Yolum, Tunga Güngör, Fikret Gürgen and Can Özturan. Istanbul, Turkey: Springer. 606--15 of Lecture Notes in Computer Science.
15. }}John P. Pestian, Christopher Brew, Pawel Matykiewicz, D. J. Hovermale, Neil Johnson, K. Bretonnel Cohen, and Włodzisław Duch. 2007. A Shared Task Involving Multi-Label Classification of Clinical Free Text. ACL: BioNLP. Prague: Association for Computational Linguistics. 97--104.
16. }}R. Bharat Rao, Sathyakama Sandilya, Radu Stefan Niculescu, Colin Germond, and Harsha Rao. 2003. Clinical and Financial Outcomes Analysis with Existing Hospital Patient Records. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: ACM Press New York, NY, USA. 416--25.
17. }}Robert E. Schapire and Yoram Singer. 2000. Boostexter: A Boosting-Based System for Text Categorization. Machine Learning 39(2):135--68.
18. }}Alan R. Shapiro. 2004. Taming Variability in Free Text: Application to Health Surveillance. MMWR. Morbidity And Mortality Weekly Report 53 Suppl:95--100.
19. }}Tawanda Carleton Sibanda, T. He, Peter Szolovits, and Özlem Uzuner. 2006. Syntactically-Informed Semantic Category Recognition in Discharge Summaries. Proceedings of the AMIA symposium. 714--8.
20. }}Stephan Spat, Bruno Cadonna, Ivo Rakovac, Christian Gütl, Hubert Leitner, Günther Stark, and Peter Beck. 2008. Enhanced Information Retrieval from Narrative German-Language Clinical Text Documents Using Automated Document Classification. Studies In Health Technology And Informatics 136:473--78.
21. }}Jim Suchan. 1995. The Influence of Organizational Metaphors on Writers' Communication Roles and Stylistic Choices. Journal of Business Communication 32(1):7--29.
22. }}Özlem Uzuner. 2008. Second I2b2 Workshop on Natural Language Processing Challenges for Clinical Records. Proceedings of the AMIA symposium:1252--53.

Index Terms

• Applied computing

  • Document management and text processing

• Computing methodologies

  • Artificial intelligence

    • Natural language processing

      • Language resources

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Supervised learning by classification

    • Machine learning approaches

      • Classification and regression trees