A general framework for neural network models on censored survival data

@article{Biganzoli2002AGF,
  title={A general framework for neural network models on censored survival data},
  author={Elia Biganzoli and Patrizia Boracchi and Ettore Marubini},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2002},
  volume={15 2},
  pages={
          209-18
        }
}
Flexible parametric techniques for regression analysis, such as those based on feed forward artificial neural networks (FFANNs), can be useful for the statistical analysis of censored time data. These techniques are of particular interest for the study of the outcome dependence from several variables measured on a continuous scale, since they allow for the detection of complex non-linear and non-additive effects. Few efforts have been made until now to account for censored times in FFANNs. In… CONTINUE READING
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