Abstract
An important problem that arises in hospitals is the monitoring and detection of nosocomial or hospital acquired infections (NIs). This paper describes a retrospective analysis of a prevalence survey of NIs done in the Geneva University Hospital. Our goal is to identify patients with one or more NIs on the basis of clinical and other data collected during the survey. In this classification task, the main difficulty resides in the significant imbalance between positive or infected (11%) and negative (89%) cases. To remedy class imbalance, we propose a novel approach in which both oversampling of rare positives and undersampling of the non infected majority rely on synthetic cases generated via class-specific subclustering. Experiments have shown this approach to be remarkably more effective than classical random resampling methods.
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References
Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.P.: Smote: Synthetic minority over-sampling technique. In: International Conference on Knowledge-Based Systems (2000)
Domingos, P.: A general method for making classifiers cost-sensitive. In: Proc. 5th International Conference on Knowledge Discovery and Data Mining, pp. 155–164 (1999)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, Chichester (2000)
French, G.G., Cheng, A.F., Wong, S.L., Donnan, S.: Repeated prevalence surveys for monitoring effectiveness of hospital infection control. Lancet 2, 1021–1023 (1983)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proc. 13th International Conference on Machine Learning (1996)
Harbarth, S., Ruef, C., Francioli, P., Widmer, A., Pittet, D., Network, S.-N.: Nosocomial infections in swiss university hospitals: a multi-centre survey and review of the published experience. Schweiz Med. Wochenschr. 129, 1521–1528 (1999)
Japkowicz, N.: The class imbalance problem: A systematic study. Intelligent Data Analysis Journal 6(5) (2002)
Joshi, M.V., Agarwal, R.C., Kuma, V.: Mining needles in a haystack: Classifying rare classes via two-phase rule induction. In: ACM-SIGMOD (2001)
Kubat, M., Matwin, S.: Addressing the curse of imbalanced data sets: Onesided sampling. In: Procs. of the Fourteenth International Conference on Machine Learning, pp. 179–186 (1997)
Manevitz, L.M., Youssef, M.: One-class SVMs for document classification. Journal of Machine Learning Research 2 (December 2001)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Vapnik, V.: Statistical Learning Theory. Wiley, Chichester (1998)
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© 2003 Springer-Verlag Berlin Heidelberg
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Cohen, G., Hilario, M., Sax, H., Hugonnet, S. (2003). Data Imbalance in Surveillance of Nosocomial Infections. In: Perner, P., Brause, R., Holzhütter, HG. (eds) Medical Data Analysis. ISMDA 2003. Lecture Notes in Computer Science, vol 2868. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39619-2_14
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DOI: https://doi.org/10.1007/978-3-540-39619-2_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20282-0
Online ISBN: 978-3-540-39619-2
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