  The accurate prediction of survival times for patients with severe diseases
remains a critical challenge despite recent advances in artificial
intelligence. This study introduces "SurvTimeSurvival: Survival Analysis On
Patients With Multiple Visits/Records", utilizing the Transformer model to not
only handle the complexities of time-varying covariates but also covariates
data. We also tackle the data sparsity issue common to survival analysis
datasets by integrating synthetic data generation into the learning process of
our model. We show that our method outperforms state-of-the-art deep learning
approaches on both covariates and time-varying covariates datasets. Our
approach aims not only to enhance the understanding of individual patient
survival trajectories across various medical conditions, thereby improving
prediction accuracy, but also to play a pivotal role in designing clinical
trials and creating new treatments.
