Assessing the eligibility of kidney transplant donors
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | , , , |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/74016 |
Resumo: | 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. |
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Assessing the eligibility of kidney transplant donorsCiência de computadores, Ciências Médicas, Ciências da computação e da informaçãoComputer science, Medical sciences, Computer and information sciencesOrgan 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.20092009-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/74016eng10.1007/978-3-642-03070-3_60Francisco ReinaldoCarlos FernandesMd. Anishur RahmanAndreia MalucelliRui Camachoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T15:18:16Zoai:repositorio-aberto.up.pt:10216/74016Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:20:10.411723Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Assessing the eligibility of kidney transplant donors |
title |
Assessing the eligibility of kidney transplant donors |
spellingShingle |
Assessing the eligibility of kidney transplant donors Francisco Reinaldo Ciência de computadores, Ciências Médicas, Ciências da computação e da informação Computer science, Medical sciences, Computer and information sciences |
title_short |
Assessing the eligibility of kidney transplant donors |
title_full |
Assessing the eligibility of kidney transplant donors |
title_fullStr |
Assessing the eligibility of kidney transplant donors |
title_full_unstemmed |
Assessing the eligibility of kidney transplant donors |
title_sort |
Assessing the eligibility of kidney transplant donors |
author |
Francisco Reinaldo |
author_facet |
Francisco Reinaldo Carlos Fernandes Md. Anishur Rahman Andreia Malucelli Rui Camacho |
author_role |
author |
author2 |
Carlos Fernandes Md. Anishur Rahman Andreia Malucelli Rui Camacho |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Francisco Reinaldo Carlos Fernandes Md. Anishur Rahman Andreia Malucelli Rui Camacho |
dc.subject.por.fl_str_mv |
Ciência de computadores, Ciências Médicas, Ciências da computação e da informação Computer science, Medical sciences, Computer and information sciences |
topic |
Ciência de computadores, Ciências Médicas, Ciências da computação e da informação Computer science, Medical sciences, Computer and information sciences |
description |
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. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009 2009-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/74016 |
url |
https://hdl.handle.net/10216/74016 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1007/978-3-642-03070-3_60 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
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1799136118861463552 |