Survival Analysis is the collection of statistical techniques used to model the time of occurrence, i.e. survival time, of an event of interest such as death, marriage, the lifespan of a consumer product or the onset of a disease. Traditional survival analysis methods rely on assumptions that make it difficult, if not impossible to learn complex non-linear relationships between the covariates and survival time that is inherent in many real world applications. We first demonstrate that a recurrent neural network (RNN) is better suited to model problems with non-linear dependencies in synthetic time-dependent and non-time-dependent experiments.
College and Department
Physical and Mathematical Sciences; Mathematics
BYU ScholarsArchive Citation
Glazier, Seth William, "Sequential Survival Analysis with Deep Learning" (2019). Theses and Dissertations. 7528.
Survival Analysis, Deep Learning, Neural Networks