Double-Blind Comparison of Survival Analysis Models Using a Bespoke Web System

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Taktak:2006:EMBS,

• author = "A. F. G. Taktak and Christian Setzkorn and B. E. Damato",
• title = "Double-Blind Comparison of Survival Analysis Models Using a Bespoke Web System",
• booktitle = "Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE",
• year = "2006",
• pages = "2466--2469",
• address = "New York",
• month = aug,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/IEMBS.2006.259797",
• abstract = "The aim of this study was to carry out a comparison of different linear and non-linear models from different centres on a common dataset in a double-blind manner to eliminate bias. The dataset was shared over the Internet using a secure bespoke environment called geoconda. Models evaluated included: (1) Cox model, (2) Log Normal model, (3) Partial Logistic Spline, (4) Partial Logistic Artificial Neural Network and (5) Radial Basis Function Networks. Graphical analysis of the various models with the Kaplan-Meier values were carried out in 3 survival groups in the test set classified according to the TNM staging system. The discrimination value for each model was determined using the area under the ROC curve. Results showed that the Cox model tended towards optimism whereas the partial logistic Neural Networks showed slight pessimism.",
• notes = "1557-170X",

}

Genetic Programming entries for Azzam F G Taktak Christian Setzkorn Bertil E Damato

Citations