Automatic development of clinical prediction models with genetic programming: A case study in cardiovascular disease

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@Article{Bannister:2014:VH,

• author = "C. A. Bannister and C. J. Currie and A. Preece and I. Spasic",
• title = "Automatic development of clinical prediction models with genetic programming: A case study in cardiovascular disease",
• journal = "Value in Health",
• volume = "17",
• number = "3",
• pages = "A200--A201",
• year = "2014",
• note = "ISPOR 19th Annual International Meeting Research Abstracts",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1098-3015",
• DOI = "doi:10.1016/j.jval.2014.03.1171",
• URL = "http://www.sciencedirect.com/science/article/pii/S1098301514012224",
• size = "0.1 pages",
• abstract = "Genetic programming is an Evolutionary Computing technique, inspired by biological evolution, capable of discovering complex non-linear patterns in large datasets. Despite the potential advantages of genetic programming over standard statistical methods, its applications to survival analysis are at best rare, primarily because of the difficulty in handling censored data. The aim of this study was to demonstrate the utility of genetic programming for the automatic development of clinical prediction models using cardiovascular disease as a case study.",
• notes = "PRM115",

}

Genetic Programming entries for Christian Bannister Craig Currie A D Preece Irena Spasic

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