Partial logistic artificial neural network for competing risks regularized with automatic relevance determination

Authors:
Paulo J. G. Lisboa;Terence A. Etchells;Ian H. Jarman;Corneliu T. C. Arsene;M. S. Hane Aung;Antonio Eleuteri;Azzam F. G. Taktak;Federico Ambrogi;Patrizia Boracchi;Elia Biganzoli
Affiliations:
School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK;School of Engineering Sciences, University of Southampton, Southampton, UK and School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK;Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK;Department of Medical Physics and Clinical Engineering, Royal Liverpool University Hospital, Liverpool, UK;Department of Medical Physics and Clinical Engineering, Royal Liverpool University Hospital, Liverpool, UK;Istituto Di Statistica Medica e Biometria, Università degli Studi Di Milano, Milan, Italy;Istituto Di Statistica Medica e Biometria, Università degli Studi Di Milano, Milan, Italy;Istituto Di Statistica Medica e Biometria, Università degli Studi Di Milano, Milan, Italy and Unita Operativa di Statistica Medica e Biometria, Instituto Nazionale per lo Studio e la Cura dei ...
Venue:
IEEE Transactions on Neural Networks
Year:
2009

Citing 13
Cited 4

Statistical modelling in GLIM

Statistical modelling in GLIM
Bayesian interpolation

Neural Computation
Bias reduction in skewed binary classification with Bayesian neural networks

Neural Networks
NETLAB: algorithms for pattern recognition

NETLAB: algorithms for pattern recognition
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
A general framework for neural network models on censored survival data

Neural Networks
A review of evidence of health benefit from artificial neural networks in medical intervention

Neural Networks
Joint modelling of cause-specific hazard functions with cubic splines: an application to a large series of breast cancer patients

Computational Statistics & Data Analysis
A novel neural network-based survival analysis model

Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Double-blind evaluation and benchmarking of survival models in a multi-centre study

Computers in Biology and Medicine
The evidence framework applied to classification networks

Neural Computation
Artificial neural network for the joint modelling of discrete cause-specific hazards

Artificial Intelligence in Medicine
A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer

Artificial Intelligence in Medicine

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

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Patient stratification with competing risks by multivariate fisher distance

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Application notes: data mining in cancer research

IEEE Computational Intelligence Magazine
Model selection with PLANN-CR-ARD

IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Time-to-event analysis is important in a wide range of applications from clinical prognosis to risk modeling for credit scoring and insurance. In risk modeling, it is sometimes required to make a simultaneous assessment of the hazard arising from two or more mutually exclusive factors. This paper applies to an existing neural network model for competing risks (PLANNCR), a Bayesian regularization with the standard approximation of the evidence to implement automatic relevance determination (PLANNCR-ARD). The theoretical framework for the model is described and its application is illustrated with reference to local and distal recurrence of breast cancer, using the data set of Veronesi et al. (1995).