Please use this identifier to cite or link to this item:
Author(s): Francisco Reinaldo
Carlos Fernandes
Md. Anishur Rahman
Andreia Malucelli
Rui Camacho
Title: Assessing the eligibility of kidney transplant donors
Issue Date: 2009
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 nd 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 in uence 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.
Subject: Ciência de computadores, Ciências Médicas, Ciências da computação e da informação
Computer science, Medical sciences, Computer and information sciences
Scientific areas: Ciências exactas e naturais::Ciências da computação e da informação
Natural sciences::Computer and information sciences
Source: Machine Learning and Data Mining in Pattern Recognition
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: openAccess
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

Files in This Item:
File Description SizeFormat 
61375.pdfAssessing the Eligibility of Kidney Transplant Donors128.56 kBAdobe PDFThumbnail

This item is licensed under a Creative Commons License Creative Commons