Transformer-Based Deep Survival Analysis

In this work, we propose a new Transformer-based survival model which estimates the patient-specific survival distribution. Our contributions are twofold. First, to the best of our knowledge, existing deep survival models use either fully connected or recurrent networks, and we are the first to apply the Transformer in survival analysis. In addition, we use ordinal regression to optimize the survival probabilities over time, and penalize randomized discordant pairs. Second, many survival models are evaluated using only the ranking metrics such as the concordance index. We propose to also use the absolute error metric that evaluates the precise duration predictions on observed subjects. We demonstrate our model on two publicly available real-world datasets, and show that our mean absolute error results are significantly better than the current models, meanwhile, it is challenging to determine the best model under the concordance index.