A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
A neural-Bayesian approach to survival analysis
1999
9th International Conference on Artificial Neural Networks: ICANN '99
unpublished
Standard survival analysis can be given a neural interpretation in terms of a multi-layered perceptron (MLP) with exponential transfer functions. More hidden units accommodate more complex relationships. The neural interpretation suggests a Bayesian analysis, which allows one to introduce sensible priors and to sample from the posterior. We also propose a method for computing p-values from the obtained ensemble of networks, because, in the end, this is the kind of information medical experts
doi:10.1049/cp:19991215
fatcat:2gvrh7su2fgzzih7izs345jg2i