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Related Experiment Videos

Survival analysis and neural nets

K Liestøl1, P K Andersen, U Andersen

  • 1Statistical Research Unit, University of Copenhagen, Denmark.

Statistics in Medicine
|June 30, 1994
PubMed
Summary
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Feed-forward neural networks offer flexible extensions to standard survival regression models. The back-propagation algorithm aids in estimating parameters for these models, proving useful in prediction and exploratory analyses.

Area of Science:

  • Biostatistics
  • Machine Learning
  • Survival Analysis

Background:

  • Survival data analysis commonly employs regression models.
  • Feed-forward neural networks present an alternative modeling approach.

Purpose of the Study:

  • To explore the application of feed-forward neural networks in survival data analysis.
  • To demonstrate how back-propagation can be used for parameter estimation in these models.

Main Methods:

  • Utilized feed-forward neural networks and the back-propagation algorithm.
  • Applied these methods to standard and generalized regression models for survival data.
  • Illustrated with examples from malignant melanoma and post-partum amenorrhoea.

Main Results:

Related Experiment Videos

  • Back-propagation enables maximum likelihood estimation in survival regression models.
  • Neural network models provide flexible extensions to standard regression techniques.
  • Demonstrated utility in prediction and exploratory analysis for survival data.

Conclusions:

  • Feed-forward neural networks are viable, flexible extensions for survival data analysis.
  • Challenges remain regarding parameter interpretation and the number of parameters.
  • These models show promise for prediction and exploratory tasks in large datasets.