Abstract
Organ transplantation is a highly complex decision process that requires expert decisions. The major problem in a transplantation procedure is the possibility of the receiver’s immune system attack and destroy the transplanted tissue. It is therefore of capital importance to find a donor with the highest possible compatibility with the receiver, and thus reduce rejection. Finding a good donor is not a straightforward task because a complex network of relations exists between the immunological and the clinical variables that influence the receiver’s acceptance of the transplanted organ. Currently the process of analyzing these variables involves a careful study by the clinical transplant team. The number and complexity of the relations between variables make the manual process very slow. In this paper we propose and compare two Machine Learning algorithms that might help the transplant team in improving and speeding up their decisions. We achieve that objective by analyzing past real cases and constructing models as set of rules. Such models are accurate and understandable by experts.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Altman, L.: Distúrbios imunes transplante (disturb immune transplant). Manual merck (2006), http://www.msd.brazil.com
SNT. Sistema nacional de transplantes (National Transplant System) (2007)
Abbas, A.: Imunologia Celular e Molecular (Cellular and Molecular Immunology), 3rd edn. Revinter (2000)
Clark, P., Boswell, R.: Rule induction with CN2: Some recent improvements. In: Kodratoff, Y. (ed.) EWSL 1991. LNCS, vol. 482, pp. 151–163. Springer, Heidelberg (1991)
Clark, P., Niblett, T.: The cn2 induction algorithm. Machine Learning 3(4), 261–283 (1989)
Srinivasan, A.: Aleph manual
Fitzwater, D.: The outcome of renal transplantation in children without prolonged pre-tranplant dialysis. Clinical Pediatrics 30, 148–152 (1991)
Wolfe, R.A.: Comparison of mortality in all patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. The New England Journal of Medicine 314, 1725–1730 (1999)
Berthoux, F.: Pre-emptive renal transplantation in adults aged over 15 years. Nephrology Dialysis Transplantation 11, 41–43 (1996)
Noronha, I.L.: Diretrizes em transplante renal (guidelines on renal transplantation) (2006), http://www.sbn.org.br/Diretrizes/tx
DF: Decreto federal num. 2.268 (federal decree issue 2,268) (June 30, 1997)
Baptista-Silva, J.: Transplante renal: cirurgia no receptor: adulto (2003), http://www.lava.med.br/livro
Antunes, L.: Imunologia Geral (General Immunology). Atheneu (1999)
Duquesnoy, R.: Hlamatchmaker: a molecularly based algorithm for histocompatibility determination. Human Immunology 63, 339–352 (2002)
Fernando, M.: Hlamatchmaker: a molecularly based algorithm for histocompatibility determination. Revista Brasileira de Cirurgia Cardiovascular (Brazilian Journal of Cardiovascular Surgery) 16 (2001)
Clark, P., Niblett, T.: Induction in noisy domains. In: Progress in Machine Learning–Proceedings of EWSL 1987: 2nd European Working Session on Learning, pp. 11–30 (1987)
Reinaldo, F., Siqueira, M.: CN2 for microsoft windows XP (2006)
Rivest, R.L.: Learning decision lists (1987)
Muggleton, S.: Inductive logic programming. In: Proceedings of the 1st Conference on Algorithmic Learning Theory, pp. 43–62
Muggleton, S., Raedt, L.D.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19(20), 629–679 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Reinaldo, F., Fernandes, C., Rahman, M.A., Malucelli, A., Camacho, R. (2009). Assessing the Eligibility of Kidney Transplant Donors. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_60
Download citation
DOI: https://doi.org/10.1007/978-3-642-03070-3_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03069-7
Online ISBN: 978-3-642-03070-3
eBook Packages: Computer ScienceComputer Science (R0)