Author:
Safdari Arezou,Frisk Erik,Holmer Olov,Krysander Mattias
Reference14 articles.
1. Cox-nnet: An Artificial neural network method for prognosis prediction of high-throughput omics data;Ching;PLOS Computational Biology,2018
2. Analysis of survival data, volume 21;Cox,1984
3. lifelines: Survival analysis in Python;Davidson-Pilon;Journal of Open Source Software,2019
4. Dhada, M., Parlikad, A.K., Steinert, O., and Lindgren, T. (2022). Weibull recurrent neural networks for failure prognosis using histogram data. Neural Comput. and Appl.
5. Holmer, O., Frisk, E., and Krysander, M. (2023). Energy-based survival models for predictive maintenance. In Proceedings of IFAC World Congress, Yokohama, Japan.