Stratification methodologies for neural networks models of survival

Ana S. Fernandes, Ian H. Jarman, Terence A. Etchells, José M. Fonseca, Elia Biganzoli, Chris Bajdik, Paulo J G Lisboa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Clinical management often relies on stratification of patients by outcome. The application of flexible non-linear time-to-event models to stratification of patient populations into different and clinically meaningful risk groups is currently an important area of research. This paper proposes a definition of prognostic index for neural network models of survival. This index underpins different stratification strategies including k-means clustering, regression trees and recursive application of the log-rank test. It was obtained with multiple imputation applied to a neural network model of survival fitted to a substantial data set for breast cancer (n=931) and was evaluated with a large out of sample data set (n=4,083). It was found that the constraint imposed by regression trees on the form of the permitted rules makes it less specific than stratifying directly from the prognostic index and deriving unconstrained low-order rules with Orthogonal Search Rule Extraction.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages989-996
Number of pages8
Volume5517 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2009
Event10th International Work-Conference on Artificial Neural Networks, IWANN 2009 - Salamanca, Spain
Duration: Jun 10 2009Jun 12 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5517 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other10th International Work-Conference on Artificial Neural Networks, IWANN 2009
CountrySpain
CitySalamanca
Period6/10/096/12/09

Keywords

  • Prognostic risk index
  • Risk stratification

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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