A new tool for survival analysis: evolutionary programming/evolutionary strategies (EP/ES) support vector regression hybrid using both censored/non-censored (event) data

被引:0
|
作者
Land, Walker H., Jr. [1 ]
Qiao, Xingye [2 ]
Margolis, Dan [1 ,3 ]
Gottlieb, Ron
机构
[1] SUNY Binghamton, Dept Bioengn, Binghamton, NY 13902 USA
[2] SUNY Binghamton, Dept Math Sci, Binghamton, NY 13902 USA
[3] Univ Arizona, Dept Radiol, Tucson, AZ 85724 USA
来源
关键词
SVRc; Evolutionary Programming; Statistical Learning Theory; Survival Analysis;
D O I
10.1016/j.procs.2011.08.050
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
While the role of survival analysis in medicine has continued to be increasingly essential in making treatment and other health care decisions, the common clinical methods used for performing these analyses, such as Cox Proportional Hazard models and Kaplan-Meier curves, have become antiquated. We have developed a new survival analysis technique of the Evolutionary Programming /Evolutionary Strategies Support Vector Regression Hybrid for censored and non-censored event data. This method provides the benefits of optimized statistical learning theory to be used as a replacement for or in addition to existing survival analysis protocols. The technique was tested on an artificially censored data from a well-known benchmark dataset as well as actual clinical data with encouraging results. (C) 2011 Published by Elsevier B. V.
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页数:6
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