  The aim of survival analysis in healthcare is to estimate the probability of
occurrence of an event, such as a patient's death in an intensive care unit
(ICU). Recent developments in deep neural networks (DNNs) for survival analysis
show the superiority of these models in comparison with other well-known models
in survival analysis applications. Ensuring the reliability and explainability
of deep survival models deployed in healthcare is a necessity. Since DNN models
often behave like a black box, their predictions might not be easily trusted by
clinicians, especially when predictions are contrary to a physician's opinion.
A deep survival model that explains and justifies its decision-making process
could potentially gain the trust of clinicians. In this research, we propose
the reverse survival model (RSM) framework that provides detailed insights into
the decision-making process of survival models. For each patient of interest,
RSM can extract similar patients from a dataset and rank them based on the most
relevant features that deep survival models rely on for their predictions.
