ML Anthology

A Neural Network Model for Prognostic Prediction

W. Nick Street
ICML 1998 pp. 540-546
/icml/1998/street1998icml-neural/

Abstract

An important and difficult prediction task in many domains, particularly medical decision making, is that of prognosis. Prognosis presents a unique set of problems to a learning system when some of the outputs are unknown. This paper presents a new approach to prognostic prediction, using ideas from nonparametric statistics to fully utilize all of the available information in a neural architecture. The technique is applied to breast cancer prognosis, resulting in flexible, accurate models that may play a role in preventing unnecessary surgeries. 1 Introduction This paper applies artificial neural network classification to the analysis of survival or lifetime data (Lee, 1992), in which the objective can be broadly defined as predicting the future time of a particular event. In this work we are concerned specifically with prognosis, that is, predicting the course of a disease. These methods are applied to breast cancer prognosis, predicting how long after surgery we can expect the disea...

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Text

Street. "A Neural Network Model for Prognostic Prediction." International Conference on Machine Learning, 1998.

Markdown

[Street. "A Neural Network Model for Prognostic Prediction." International Conference on Machine Learning, 1998.](https://mlanthology.org/icml/1998/street1998icml-neural/)

BibTeX

@inproceedings{street1998icml-neural,
  title     = {{A Neural Network Model for Prognostic Prediction}},
  author    = {Street, W. Nick},
  booktitle = {International Conference on Machine Learning},
  year      = {1998},
  pages     = {540-546},
  url       = {https://mlanthology.org/icml/1998/street1998icml-neural/}
}