Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31097
Artificial Intelligence Support for Interferon Treatment Decision in Chronic Hepatitis B

Authors: Alexandru George Floares


Chronic hepatitis B can evolve to cirrhosis and liver cancer. Interferon is the only effective treatment, for carefully selected patients, but it is very expensive. Some of the selection criteria are based on liver biopsy, an invasive, costly and painful medical procedure. Therefore, developing efficient non-invasive selection systems, could be in the patients benefit and also save money. We investigated the possibility to create intelligent systems to assist the Interferon therapeutical decision, mainly by predicting with acceptable accuracy the results of the biopsy. We used a knowledge discovery in integrated medical data - imaging, clinical, and laboratory data. The resulted intelligent systems, tested on 500 patients with chronic hepatitis B, based on C5.0 decision trees and boosting, predict with 100% accuracy the results of the liver biopsy. Also, by integrating the other patients selection criteria, they offer a non-invasive support for the correct Interferon therapeutic decision. To our best knowledge, these decision systems outperformed all similar systems published in the literature, and offer a realistic opportunity to replace liver biopsy in this medical context.

Keywords: Interferon, chronic hepatitis B, intelligent virtualbiopsy

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1178


[1] A. Lindor, "The role of ultrasonography and automatic-needle biopsy in outpatient percutaneous liver biopsy," Hepatology, vol. 23, pp. 1079- 1083, 1996.
[2] A. Tobkes and H. J. Nord, "Liver biopsy: Review of methodology and complications.," Digestive Disorders, vol. 13, pp. 267-274, 1995.
[3] A. G. Floares, M. Lupsor, H. Stefanescu, Z. Sparchez, A. Serban, T. Suteu, and R. Badea, "Toward intelligent virtual biopsy: Using artificial intelligence to predict fibrosis stage in chronic hepatitis c patients without biopsy," Journal of Hepatology, vol. 48, no. 2, 2008.
[4] A. Floares, M. Lupsor, H. Stefanescu, Z. Sparchez, R. Badea, and Romania, "Intelligent virtual biopsy can predict fibrosis stage in chronic hepatitis c, combining ultrasonographic and laboratory parameters, with 100% accuracy," Proceedings of The XXth Congress of European Federation of Societies for Ultrasound in Medicine and Biology, 2008.
[5] I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, Feature Extraction: Foundations and Applications. Studies in Fuzziness and Soft Computing, Springer, August 2006.
[6] J. V. Hulse, T. M. Khoshgoftaar, and A. Napolitano, "Experimental perspectives on learning from imbalanced data," in Proceedings of the 24 th International Conference on Machine Learning, (Corvallis, OR), 2007.
[7] J. Quinlan, C4.5 : Programs for Machine Learning. Morgan Kaufmann, 1993.
[8] L. Breiman, "Bagging predictors," Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
[9] Y. Freund and R. E. Schapire, "A decisiontheoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119-139, 1997.
[10] T. Fawcett, "Roc graphs: Notes and practical considerations for researchers. technical report," tech. rep., Palo Alto, USA: HP Laboratories, 2004.
[11] T. Poynard, R. Morra, P. Halfon, L. Castera, V. Ratziu, F. Imbert- Bismut, S. Naveau, D. Thabut, D. Lebrec, F. Zoulim, M. Bourliere, P. Cacoub, D. Messous, M. Muntenau, and V. de Ledinghen, "Metaanalyses of fibrotest diagnostic value in chronic liver disease," BMC Gastroenterology, vol. 7, no. 40, 2007.