Assessing the Eligibility of Kidney Transplant Donors

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2009

Autores

Reinaldo, F (Autor) Outra; Fernandes, C (Autor) Outra; Rahman, MA (Autor) Outra; Malucelli, A (Autor) Outra; Rui Camacho (Autor) FEUP

Ata de Conferência Internacional

Título: Machine Learning and Data Mining in Pattern Recognition

Páginas: 802-809

6th International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM 2009)

Leipzig, Germany, 23-25 July, 2009

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-003-R7S

DOI: 10.1007/978-3-642-03070-3_60

Abstract (EN): Organ transplantation is a highly complex decision process that requires expert, decisions. The major problem ill 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 exist's between the immunological and the clinical variables that, influence the receivers 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. Ill this paper we propose and compare two Machine Learning algorithms that might help the transplant team ill 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8