Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach.

@article{Biganzoli1998FeedFN,
  title={Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach.},
  author={Elia Biganzoli and Patrizia Boracchi and Luigi Mariani and Ettore Marubini},
  journal={Statistics in medicine},
  year={1998},
  volume={17 10},
  pages={1169-86}
}
Flexible modelling in survival analysis can be useful both for exploratory and predictive purposes. Feed forward neural networks were recently considered for flexible non-linear modelling of censored survival data through the generalization of both discrete and continuous time models. We show that by treating the time interval as an input variable in a standard feed forward network with logistic activation and entropy error function, it is possible to estimate smoothed discrete hazards as… CONTINUE READING
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