  Interpretability is crucial for doctors, hospitals, pharmaceutical companies
and biotechnology corporations to analyze and make decisions for high stakes
problems that involve human health. Tree-based methods have been widely adopted
for survival analysis due to their appealing interpretablility and their
ability to capture complex relationships. However, most existing methods to
produce survival trees rely on heuristic (or greedy) algorithms, which risk
producing sub-optimal models. We present a dynamic-programming-with-bounds
approach that finds provably-optimal sparse survival tree models, frequently in
only a few seconds.
