Partial logistic artificial neural networks (PLANN) for flexible modeling of censored survival data

Elia M. Biganzoli, Federico Ambrogi, Patrizia Boracchi

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

Abstract

Linear and non-linear flexible regression analysis techniques, such as those based on splines and feed forward artificial neural networks (FFANN), have been proposed for the statistical analysis of censored survival time data, to account for the presence of non linear effects of predictors. Among survival functions, the hazard has a biological interest for the study of the disease dynamics, moreover it allows for the estimation of cumulative incidence functions for predicting outcome probabilities over follow-up. Therefore, specific error functions and data representation have been introduced for FFANN extensions of generalized linear models, in the perspective of modelling the hazard function of censored survival data. These techniques can be applied to account for the prognostic contribution of new biomarkers in addition to the traditional ones.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages340-346
Number of pages7
DOIs
Publication statusPublished - 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: Jun 14 2009Jun 19 2009

Other

Other2009 International Joint Conference on Neural Networks, IJCNN 2009
CountryUnited States
CityAtlanta, GA
Period6/14/096/19/09

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Biganzoli, E. M., Ambrogi, F., & Boracchi, P. (2009). Partial logistic artificial neural networks (PLANN) for flexible modeling of censored survival data. In Proceedings of the International Joint Conference on Neural Networks (pp. 340-346). [5178824] https://doi.org/10.1109/IJCNN.2009.5178824