  In this manuscript we analyze a data set containing information on children
with Hodgkin Lymphoma (HL) enrolled on a clinical trial. Treatments received
and survival status were collected together with other covariates such as
demographics and clinical measurements. Our main task is to explore the
potential of machine learning (ML) algorithms in a survival analysis context in
order to improve over the Cox Proportional Hazard (CoxPH) model. We discuss the
weaknesses of the CoxPH model we would like to improve upon and then we
introduce multiple algorithms, from well-established ones to state-of-the-art
models, that solve these issues. We then compare every model according to the
concordance index and the brier score. Finally, we produce a series of
recommendations, based on our experience, for practitioners that would like to
benefit from the recent advances in artificial intelligence.
