We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Recent Trends in Computational Prediction of Renal Transplantation Outcomes

by Aswathy Ravikumar, Saritha R, Vinod Chandra S S
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 12
Year of Publication: 2013
Authors: Aswathy Ravikumar, Saritha R, Vinod Chandra S S
10.5120/10521-5501

Aswathy Ravikumar, Saritha R, Vinod Chandra S S . Recent Trends in Computational Prediction of Renal Transplantation Outcomes. International Journal of Computer Applications. 63, 12 ( February 2013), 33-37. DOI=10.5120/10521-5501

@article{ 10.5120/10521-5501,
author = { Aswathy Ravikumar, Saritha R, Vinod Chandra S S },
title = { Recent Trends in Computational Prediction of Renal Transplantation Outcomes },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 12 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number12/10521-5501/ },
doi = { 10.5120/10521-5501 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:14:11.161610+05:30
%A Aswathy Ravikumar
%A Saritha R
%A Vinod Chandra S S
%T Recent Trends in Computational Prediction of Renal Transplantation Outcomes
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 12
%P 33-37
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Renal transplantation has become the treatment of choice for most patients with end-stage renal disease. Recent advances in renal transplantation notably, the matching of Major Histocompatibility Complex (MHC) and improved immunosuppressants have improved short-term and long-term graft survival rates. In light of recent developments optimization of kidney transplant outcomes is paramount to further augment the graft survival time and the quality of life of the patient. An intuitive understanding of the post transplantation interaction mechanisms involving graft and host is intricate and on account of this prognosis of planned organ transplantation outcomes is an involved problem. Consequently, machine learning approaches based on donor and recipient data are indespensible for improved prognosis of graft outcomes. This study proposes improved data mining-based models for variable filtering and for prediction of graft status and survival period in renal transplantation using the patient profile information prior to the transplantation.

References
  1. Abouna, G. M. (2003). Ethical issues in organ transplantation. Medical Principle and Practice, 12, 54-69.
  2. Sheppard, D. , McPhee, D. , Darke, C. , Shrethra, B. , Moore, R. , Jurewitz, A. , & Gray, A. (1999). Predicting cytomegalovirus disease after renal transplantation: An artificial neural network approach". International Journal of Medical Informatics, 54(1), 55-76.
  3. Lin, H. M. , Kauffman, H. M. , McBride, M. A. , Davies, D. B. , Rosendale, J. D. , Smith, C. M. , Edwards, E. B. , Daily, O. P. , Kirklin, J. , Shield, C. F. , & Hunsicker, L. G. (1998). Center-specific graft and patient survival rates: 1997 UNOS report. The Journal of the American Medical Association (JAMA), 280(13), 1153-1160.
  4. Locatelli, F. and al. , Nephrology: Main Advances in the Last 40 Years, Journal of Nephrology, Vol. 19, p. 6-11 . , 2006.
  5. J. M. A. Smith, J. Vanhaecke, A. Haverich, E. De Veries, L. Roels, G. Persijn, G. Laufer, Waiting for a thoracic transplant in eurotransplant, Transplant International 19 (2006).
  6. IBM SPSS Modeler, A comprehensive data/text mining software environment,version 14. 08.
  7. F. L. Grover, M. L. Barr, L. B. Edwards, F. J. Martinez, R. N. Pierson, B. R. Rosengard, S. Murray, Thoracic transplantation, American Journal of Transplantation 3 (2003)91–102.
  8. M. Schmitt, H. N. Teodorescu, A. Jain, S. Jain, L. C. Jain, Computational intelligence processing in medical processing, Studies in Fuzziness and Soft Computing,Springer-Verlag, 2002.
  9. Hariharan S, Johnson CP, Bresnahan BA, Taranto SE, McIntosh MJ, Stablein D. "Improved graft survival after renal transplantation in the United States, 1988 to 1996. " ,The New England Journal of Medicine 2000;342:605–12.
  10. Herrero JI, Lucena JF, Quiroga J, Sangro B, Pardo F, Rotellar F, et al. "Liver transplant recipients older than 60 years have lower survival and higher incidence of malignancy. ", American Journal of Transplantation 2003;3:1407–12.
  11. Hong Z, WuJ, Smart G, Kaita K, Wen SW, Paton S, et al. Survival analysis of liver transplant patients in Canada. Transplantation Proceedings 2006;38:2951–6.
  12. Kusiak A, Dixon B, Shah S. "Predicting survival time for kidney dialysis patients: a data mining approach". Computers in Biology and Medicine 2005;35:311–27.
  13. Jenkins PC, Flanagan MF, Jenkins KJ, Sargent JD, Canter CE, Chinnock RE, et al. "Survival analysis and risk factors for mortality in transplantation and staged surgery for hypoplastic left heart syndrome. ", Journal of the American College of Cardiology 2000;36:1178–85.
  14. Asil Oztekin, "An Analytical Approach to Predict the Performance of Thoracic Transplantations" ,JCC: The Business and Economics Research Journal ,Volume 5, Issue 2, 2012 , 185-206.
  15. Sarah E. Taranto,Ann M. Harper,Erick B. dwards,John D. Rosendale, Maureen A. McBride,0. Patrick Daily, "Developing a National Allocation Model For Cadaveric Kidneys", Proceedings of the 2000 Winter Simulation Conference.
  16. Asil Oztekin, Dursun Delen,Zhenyu, " Predicting the graft survival for heart–lung transplantation patients: An integrated data mining methodology", international journal of medical informatics 7 8 ( 2 0 0 9 ) e84–e96.
  17. N. Petrovsky, S. K. Tam, V. Brusic, G. Russ, L. Socha, and V. B. Bajic,"Use of Artificial Neural Networks in Improving Renal Transplantation Outcomes Outcomes," Graft, Vol. 5, Issue 1, pp. 6-13, 2002.
  18. S. K. Agarwal, R. K. Srivastava,, "Chronic Kidney Disease in India: Challenges and Solutions", Nephron Clin Pract 2009;111:c197-c203.
  19. R. AWolfe, K. P. Mcmullough et. al. "Calculating life years from Transplant Methods for kidney and kidney pancreas candidates", American Journal of Transplantation 2008; 8 (Part 2): 997–1011.
  20. Jiakai Li, Gursel Serpen, Steven Selman et. al. "Bayes Net Classifiers for Prediction of Renal Graft Status and Survival Period", World Academy of Science, Engineering and Technology 39 2010.
  21. N. Hoot, "Models to Predict Survival After Liver Transplantation," M. S. thesis, Vanderbilt University , Nashville, Tennessee, USA, 2005.
  22. J. -H. Ahn, J. -W. Kwon and Y. -S. Lee, "Prediction of 1-year Graft Survival Rates in Kidney Transplantation: A Bayesian Network Model," in Proc. INFORMS & KORMS, Seoul, Korea, 2000, pp. 505-513.
Index Terms

Computer Science
Information Sciences

Keywords

Prediction model Survival analysis machine learning Data mining Renal Transplantation