Fornili, M., Ambrogi, F., Boracchi, P. and Biganzoli, E. (2014) Piecewise Exponential Artificial Neural Networks (PEANN) for Modeling Hazard Function with Right Censored Data. In: Formenti, E., Tagliaferri, R. and Wit, E., Eds., Computational Intelligence Methods for Bioinformatics and Biostatistics, Springer, Berlin, 125-136.
https://doi.org/10.1007/978-3-319-09042-9_9

has been cited by the following article:

• TITLE: Artificial Neural Network Model for Predicting Lung Cancer Survival

  AUTHORS: Hansapani Rodrigo, Chris P. Tsokos

  KEYWORDS: Survival Analysis, Hazard Prediction, Artificial Neural Network, Piecewise Exponential Survival Model, Censored Data, Lung Cancer

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.5 No.1, February 22, 2017

  ABSTRACT: The object of our present study is to develop a piecewise constant hazard model by using an Artificial Neural Network (ANN) to capture the complex shapes of the hazard functions, which cannot be achieved with conventional survival analysis models like Cox proportional hazard. We propose a more convenient approach to the PEANN created by Fornili et al. to handle a large amount of data. In particular, it provides much better prediction accuracies over both the Poisson regression and generalized estimating equations. This has been demonstrated with lung cancer patient data taken from the Surveillance, Epidemiology and End Results (SEER) program. The quality of the proposed model is evaluated by using several error measurement criteria.