AUTOMATIC IDENTIFICATION OF NEGATED CONCEPTS IN NARRATIVE CLINICAL REPORTS

Lior Rokach, Roni Romano, Oded Maimon

Abstract

Substantial medical data such as discharge summaries and operative reports are stored in textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. Terms that appear in these documents tend to appear in different contexts. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are those denied by the patient or subsequently “ruled out.” Hence, when searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. In this paper we examine the applicability of machine learning methods for automatic identification of negative context patterns in clinical narratives reports. We suggest two new simple algorithms and compare their performance with standard machine learning techniques such as neural networks and decision trees. The proposed algorithms significantly improve the performance of information retrieval done on medical narratives.

References

  1. Aronow D, Feng F, Croft WB. Ad Hoc Classification of Radiology Reports. Journal of the American Medical Informatics Association 1999; 6(5): 393-411.
  2. Averbuch M, Karson T, Ben-Ami B, Maimon O. and Rokach L., Context-Sensitive Medical Information Retrieval, MEDINFO-2004, San Francisco, CA, September 2004, IOS Press, pp. 282-286.
  3. Cessie S. and van Houwelingen, J.C. , Ridge Estimators in Logistic Regression. Applied Statistics 1997: 41 (1): 191-201.
  4. Chapman W.W., Bridewell W., Hanbury P, Cooper GF, Buchanann BG. A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries. J. Biomedical Info. 2001: 34: 301-310.
  5. Duda R. and Hart P., Pattern Classification and Scene Analysis. Wiley, New York, 1973.
  6. Fiszman M., Chapman W.W., Aronsky D., Evans RS, Haug PJ., Automatic detection of acute bacterial pneumonia from chest X-ray reports. J Am Med Inform Assoc 2000; 7:593-604.
  7. Fiszman M., Haug P.J., Using medical language processing to support real-time evaluation of pneumonia guidelines. Proc AMIA Symp 2000; 235- 239.
  8. Friedman C., Alderson P, Austin J, Cimino J, Johnson S. A General Natural-Language Text Processor for Clinical Radiology. Journal of the American Medical Informatics Association 1994; 1(2): 161-74.
  9. Hersh WR, Hickam DH. Information retrieval in medicine: the SAPHIRE experience. J. of the Am Society of Information Science 1995: 46:743-7.
  10. Hripcsak G, Knirsch CA, Jain NL, Stazesky RC, Pablosmendez A, Fulmer T. A health information network for managing innercity tuberculosis: bridging clinical care, public health, and home care. Comput Biomed Res 1999; 32:67-76.
  11. Keerthi S.S., Shevade S.K., Bhattacharyya C., Murth K.R.K., Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation 2001: 13(3):637-649.
  12. Lindbergh D.A.B., Humphreys B.L., The Unified Medical Language System. In: van Bemmel JH and McCray AT, eds. 1993 Yearbook of Medical Informatics. IMIA, the Netherlands, 1993; pp. 41-51.
  13. Mutalik P.G., Deshpande A., Nadkarni PM. Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. J Am Med Inform Assoc 2001: 8(6): 598-609.
  14. Myers E., An O(ND) difference algorithm and its variations, Algorithmica Vol. 1 No. 2, 1986, p 251.
  15. Nadkarni P., Information retrieval in medicine: overview and applications. J. Postgraduate Med. 2000: 46 (2).
  16. Pratt A.W. Medicine, computers, and linguistics. Advanced Biomedical Engineering 1973: 3:97-140.
  17. Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
  18. Rokach L., Averbuch M., Maimon O., Information Retrieval System for Medical Narrative Reports, Lecture Notes in Artificial intelligence 3055, pp. 217- 228 Springer-Verlag, 2004.
  19. Sebastiani F., Machine learning in automated text categorization. ACM Comp. Surv., 34(1):1-47, 2002.
  20. Van Rijsbergen, CJ.. Information Retrieval. 2nd edition, London, Butterworths, 1979.
Download


Paper Citation


in Harvard Style

Rokach L., Romano R. and Maimon O. (2006). AUTOMATIC IDENTIFICATION OF NEGATED CONCEPTS IN NARRATIVE CLINICAL REPORTS . In Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-972-8865-42-9, pages 257-262. DOI: 10.5220/0002497702570262


in Bibtex Style

@conference{iceis06,
author={Lior Rokach and Roni Romano and Oded Maimon},
title={AUTOMATIC IDENTIFICATION OF NEGATED CONCEPTS IN NARRATIVE CLINICAL REPORTS},
booktitle={Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2006},
pages={257-262},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002497702570262},
isbn={978-972-8865-42-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - AUTOMATIC IDENTIFICATION OF NEGATED CONCEPTS IN NARRATIVE CLINICAL REPORTS
SN - 978-972-8865-42-9
AU - Rokach L.
AU - Romano R.
AU - Maimon O.
PY - 2006
SP - 257
EP - 262
DO - 10.5220/0002497702570262