Partial logistic artificial neural network for competing risks regularized with automatic relevance determination.
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 (1995).
Lisboa
Paulo J G
PJ
School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool L33AF, UK. p.j.lisboa@ljmu.ac.uk
Etchells
Terence A
TA
Jarman
Ian H
IH
Arsene
Corneliu T C
CT
Aung
M S Hane
MS
Eleuteri
Antonio
A
Taktak
Azzam F G
AF
Ambrogi
Federico
F
Boracchi
Patrizia
P
Biganzoli
Elia
E
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001331:Automation / Q000379:methods*; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000175:diagnosis; D003198:Computer Simulation; D016208:Databases, Factual; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011336:Probability; D016016:Proportional Hazards Models; D012306:Risk; D016019:Survival Analysis; D013997:Time Factors; D055815:Young Adult