2021/05/18 by Sautreuil, Mathilde, Lemler, Sarah, Cournède, Paul-Henry
#FOS: Mathematics #Statistics Theory (math.ST)
paper · doi:10.48550/arxiv.2105.08338
Survival analysis consists of studying the elapsed time until an event of interest, such as the death or recovery of a patient in medical studies. This work explores the potential of neural networks in survival analysis from clinical and RNA-seq data. If the neural network approach is not recent in survival analysis, methods were classically considered for low-dimensional input data. But with the emergence of high-throughput sequencing data, the number of covariates of interest has become very large, with new statistical issues to consider. We present and test a few recent neural network approaches for survival analysis adapted to high-dimensional inputs.