Artificial Intelligence Support for Interferon Treatment Decision in Chronic Hepatitis B

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 proce- dure. 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 virtual biopsy.

[1]  Patrice Cacoub,et al.  Meta-analyses of FibroTest diagnostic value in chronic liver disease , 2007, BMC gastroenterology.

[2]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[3]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .

[4]  Taghi M. Khoshgoftaar,et al.  Experimental perspectives on learning from imbalanced data , 2007, ICML '07.

[5]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[6]  Masoud Nikravesh,et al.  Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing) , 2006 .

[7]  Masoud Nikravesh,et al.  Feature Extraction - Foundations and Applications , 2006, Feature Extraction.

[8]  A. Tobkes,et al.  Liver biopsy: review of methodology and complications. , 1995, Digestive diseases.

[9]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[10]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[11]  Radu Badea,et al.  732 TOWARD INTELLIGENT VIRTUAL BIOPSY: USING ARTIFICIAL INTELLIGENCE TO PREDICT FIBROSIS STAGE IN CHRONIC HEPATITIS C PATIENTS WITHOUT BIOPSY , 2008 .

[12]  J Rakela,et al.  The role of ultrasonography and automatic‐needle biopsy in outpatient percutaneous liver biopsy , 1996, Hepatology.

[13]  Radu Badea,et al.  Intelligent virtual biopsy can predict fibrosis stage in chronic hepatitis C, combining ultrasonographic and laboratory parameters, with 100% accuracy , 2008 .

[14]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[15]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.