Application of the Intelligent Techniques in Transplantation Databases: A Review of Articles Published in 2009 and 2010

Application of the Intelligent Techniques in Transplantation Databases: A Review of Articles Published in 2009 and 2010

Author Sousa, Fernando Sequeira Autor UNIFESP Google Scholar
Hummel, Anderson Diniz Autor UNIFESP Google Scholar
Maciel, Rafael Fabio Autor UNIFESP Google Scholar
Cohrs, Frederico Molina Autor UNIFESP Google Scholar
Falcão, Alex Esteves Jaccoud Autor UNIFESP Google Scholar
Teixeira, Fabio Oliveira Autor UNIFESP Google Scholar
Baptista, Roberto Silva Autor UNIFESP Google Scholar
Mancini, Felipe Autor UNIFESP Google Scholar
Costa, Thiago Martini da Autor UNIFESP Google Scholar
Alves, Domingos Autor UNIFESP Google Scholar
Pisa, Ivan Torres Autor UNIFESP Google Scholar
Institution Universidade Federal de São Paulo (UNIFESP)
Universidade de São Paulo (USP)
Abstract The replacement of defective organs with healthy ones is an old problem, but only a few years ago was this issue put into practice. Improvements in the whole transplantation process have been increasingly important in clinical practice. in this context are clinical decision support systems (CDSSs), which have reflected a significant amount of work to use mathematical and intelligent techniques. the aim of this article was to present consideration of intelligent techniques used in recent years (2009 and 2010) to analyze organ transplant databases. To this end, we performed a search of the PubMed and Institute for Scientific Information (ISI) Web of Knowledge databases to find articles published in 2009 and 2010 about intelligent techniques applied to transplantation databases. Among 69 retrieved articles, we chose according to inclusion and exclusion criteria. the main techniques were: Artificial Neural Networks (ANN), Logistic Regression (LR), Decision Trees (DT), Markov Models (MM), and Bayesian Networks (BN). Most articles used ANN. Some publications described comparisons between techniques or the use of various techniques together. the use of intelligent techniques to extract knowledge from databases of healthcare is increasingly common. Although authors preferred to use ANN, statistical techniques were equally effective for this enterprise.
Language English
Date 2011-05-01
Published in Transplantation Proceedings. New York: Elsevier B.V., v. 43, n. 4, p. 1340-1342, 2011.
ISSN 0041-1345 (Sherpa/Romeo, impact factor)
Publisher Elsevier B.V.
Extent 1340-1342
Origin http://dx.doi.org/10.1016/j.transproceed.2011.02.028
Access rights Open access Open Access
Type Article
Web of Science ID WOS:000291289400101
URI http://repositorio.unifesp.br/handle/11600/33648

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