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 a new pattern learning method for automatic identification of negative context in clinical narratives reports. We compare the new algorithm to previous methods proposed for the same task of similar medical narratives and show its advantages. The new algorithm can be applied also to further context identification and information extraction tasks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aronow, D., Feng, F., Croft, W.B.: Ad Hoc Classification of Radiology Reports. Journal of the American Medical Informatics Association 6(5), 393–411 (1999)
Averbuch, M., Karson, T., Ben-Ami, B., Maimon, O., Rokach, L.: Context-Sensitive Medical Information Retrieval. In: MEDINFO 2004, San Francisco, CA, September 2004, pp. 282–286. IOS Press, Amsterdam (2004)
Cessie, S., van Houwelingen, J.C.: Ridge Estimators in Logistic Regression. Applied Statistics 41(1), 191–201 (1997)
Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., Buchanann, B.G.: A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries. J. Biomedical Info. 34, 301–310 (2001)
Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Fiszman, M., Chapman, W.W., Aronsky, D., Evans, R.S., Haug, P.J.: Automatic detection of acute bacterial pneumonia from chest X-ray reports. J. Am. Med. Inform. Assoc. 7, 593–604 (2000)
Fiszman, M., Haug, P.J.: Using medical language processing to support real-time evaluation of pneumonia guidelines. In: Proc. AMIA Symp., pp. 235–239 (2000)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Machine Learning: Proceedings of the Thirteenth International Conference, pp. 325–332 (1996)
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 1(2), 161–174 (1994)
Hall, M.: Correlation- based Feature Selection for Machine Learning. Ph.D. Thesis, University of Waikato (1999)
Hersh, W.R., Hickam, D.H.: Information retrieval in medicine: the SAPHIRE experience. J. of the Am. Society of Information Science 46, 743–747 (1995)
Hripcsak, G., Knirsch, C.A., Jain, N.L., Stazesky, R.C., Pablos-mendez, A., Fulmer, T.: A health in-formation network for managing innercity tuberculosis: bridging clinical care, public health, and home care. Comput. Biomed. Res. 32, 67–76 (1999)
Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murth, K.R.K.: Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation 13(3), 637–649 (2001)
Lindbergh, D.A.B., Humphreys, B.L.: The Unified Medical Language System. In: van Bemmel, J.H., McCray, A.T. (eds.) Yearbook of Medical Informatics, pp. 41–51. IMIA, Netherlands (1993)
Mutalik, P.G., Deshpande, A., Nadkarni, P.M.: Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. J. Am. Med. Inform. Assoc. 8(6), 598–609 (2001)
Myers, E.: An O(ND) difference algorithm and its variations. Algorithmica 1(2), 251 (1986)
Nadkarni, P.: Information retrieval in medicine: overview and applications. J. Postgraduate Med. 46(2) (2000)
Pratt, A.W.: Medicine, computers, and linguistics. Advanced Biomedical Engineering 3, 97–140 (1973)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Rokach, L., Averbuch, M., Maimon, O.: Information Retrieval System for Medical Narrative Reports. In: Christiansen, H., Hacid, M.-S., Andreasen, T., Larsen, H.L. (eds.) FQAS 2004. LNCS (LNAI), vol. 3055, pp. 217–228. Springer, Heidelberg (2004)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Romano, R., Rokach, L., Maimon, O. (2006). Automatic Discovery of Regular Expression Patterns Representing Negated Findings in Medical Narrative Reports. In: Etzion, O., Kuflik, T., Motro, A. (eds) Next Generation Information Technologies and Systems. NGITS 2006. Lecture Notes in Computer Science, vol 4032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11780991_26
Download citation
DOI: https://doi.org/10.1007/11780991_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35472-7
Online ISBN: 978-3-540-35473-4
eBook Packages: Computer ScienceComputer Science (R0)