A novel method for diagnosing cirrhosis in patients with chronic hepatitis B: Artificial neural network approach

Journal of Medical Systems

Volumn 35, Issue 1, 2011, Pages 121-126

  Raoufy, Mohammad Reza ^a   Vahdani, Parviz ^b   Alavian, Seyed Moayed ^c   Fekri, Sahba ^d   Eftekhari, Parivash ^a   Gharibzadeh, Shahriar ^e  

^a TARBIAT MODARES UNIVERSITY   (Iran)

^b Department of Infectious Diseases   (Iran)

^c BAQIYATALLAH UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^d SHAHID BEHESHTI UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^e NONE

Author keywords

Artificial neural network; Chronic hepatitis B; Cirrhosis; Laboratory data; Logistic regression

Indexed keywords

ALANINE AMINOTRANSFERASE; ASPARTATE AMINOTRANSFERASE; BILIRUBIN;

ADULT; AGE; ALANINE AMINOTRANSFERASE BLOOD LEVEL; ARTICLE; ARTIFICIAL NEURAL NETWORK; ASPARTATE AMINOTRANSFERASE BLOOD LEVEL; BILIRUBIN BLOOD LEVEL; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; HEPATITIS B; HUMAN; INTERNATIONAL NORMALIZED RATIO; LIVER BIOPSY; LIVER CIRRHOSIS; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; NON INVASIVE PROCEDURE; RECEIVER OPERATING CHARACTERISTIC; THROMBOCYTE COUNT;

EID: 79952449677     PISSN: 01485598     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10916-009-9348-8     Document Type: Article

References (17)

1. Dennis, L. K., Eugene, B., Anthony, F., Stephen, H., Dan, L., and Larry, J., Harrison', s principles of internal medicine, 16th edition. Mc Graw Hill, New York, 2005.
2. J. J. Heidelbaugh M. Bruderly 2006 Cirrhosis and chronic liver failure: part I. Diagnosis and evaluation Am. Fam. Phys. 74 5 756 762
3. W. F. Leemans H. L. Janssen R. A. de Man 2007 Future prospectives for the management of chronic hepatitis B World J. Gastroenterol. 13 18 2554 2567 (Pubitemid 46938824)
4. U. Weickert A. Buttmann R. Jakobs D. Schilling A. Eickhoff J. F. Riemann 2005 Diagnosis of liver cirrhosis: a comparison of modified ultrasound and laparoscopy in 100 consecutive patients J. Clin. Gastroenterol. 39 6 529 532 10.1097/01.mcg.0000165669.17649.20 (Pubitemid 40863649)
5. J. H. Lefkowitch 2007 Liver biopsy assessment in chronic hepatitis Arch. Med. Res. 38 6 634 643 10.1016/j.arcmed.2006.08.005 (Pubitemid 47001563)
6. G. Sebastiani A. Alberti 2006 Non invasive fibrosis biomarkers reduce but not substitute the need for liver biopsy World J. Gastroenterol. 12 23 3682 3694 (Pubitemid 43950946)
7. M. Munteanu 2007 Non-invasive biomarkers FibroTest-ActiTest for replacing invasive liver biopsy: the need for change and action J. Gastrointestin. Liver Dis. 16 2 173 174 (Pubitemid 47011911)
8. S. Dariani M. Keshavarz M. Parviz M. R. Raoufy S. Gharibzadeh 2007 Modeling force-velocity relation in skeletal muscle isotonic contraction using an artificial neural network Biosystems 90 2 529 534 10.1016/j.biosystems.2006. 12.004 (Pubitemid 47364812)
9. J. V. Tu 1996 Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes J. Clin. Epidemiol. 49 1225 1231 10.1016/S0895-4356(96)00002-9 (Pubitemid 26386228)
10. J. A. Hanley B. J. McNeil 1982 The meaning and use of the area under a receiver operating characteristic (ROC) curve Radiology 143 29 36 (Pubitemid 12142173)
11. A. S. Lok B. J. McMahon 2004 Chronic hepatitis B: update of recommendations Hepatology 39 857 861 10.1002/hep.20110 (Pubitemid 38337668)
12. Chinese Society of Infectious Diseases, Chinese Medical Association Chinese Society of Hepatology, Chinese Medical Association 2007 Guideline on prevention and treatment of chronic hepatitis B in China (2005) Chin. Med. J. (Engl) 120 24 2159 2173
13. G. H. Haydon R. Jalan M. Ala-Korpela Y. Hiltunen J. Hanley L. M. Jarvis C. A. Ludlum P. C. Hayes 1998 Prediction of cirrhosis in patients with chronic hepatitis C infection by artificial neural network analysis of virus and clinical factors J. Viral. Hepat. 5 4 255 264 10.1046/j.1365-2893.1998.00108.x (Pubitemid 28358229)
14. T. C. Poon A. Y. Hui H. L. Chan I. L. Ang S. M. Chow N. Wong J. J. Sung 2005 Prediction of liver fibrosis and cirrhosis in chronic hepatitis B infection by serum proteomic fingerprinting: a pilot study Clin. Chem. 51 2 328 335 10.1373/clinchem.2004.041764 (Pubitemid 40175802)
15. F. Piscaglia A. Cucchetti S. Benlloch M. Vivarelli J. Berenguer L. Bolondi, et al. 2006 Prediction of significant fibrosis in hepatitis C virus infected liver transplant recipients by artificial neural network analysis of clinical factors Eur. J. Gastroenterol. Hepatol. 18 12 1255 1261 10.1097/01.meg.0000243885.55562.7e (Pubitemid 44737055)
16. J. A. Swets 1988 Measuring the accuracy of diagnostic systems Science 240 1285 1293 10.1126/science.3287615 965496
17. Lin, C. S., Li, Y. C., Mok, M. S., Wu, C. C., Chiu, H. W., and Lin, Y. H., Neural network modeling to predict the hypnotic effect of propofol bolus induction. Proc. AMIA Symp. 450-454, 2002.