text: The authors tackle the problem of estimating risk in a survival analysis setting with competing risks. They propose directly optimizing the time-dependent discrimination index using a siamese survival network. Experiments on several real-world dataset reveal modest gains in comparison with the state of the art. - The authors should clearly highlight what is their main technical contribution. For example, Eqs. 1-6 appear to be background material since the time-dependent discrimination index is taken from the literature, as the authors point out earlier. However, this is unclear from the writing. - One of the main motivations of the authors is to propose a model that is specially design to avoid the nonidentifiability issue in an scenario with competing risks. It is unclear why the authors solution is able to solve such an issue, specially given the modest reported gains in comparison with several competitive baselines. In other words, the authors oversell their own work, specially in comparison with the state of the art. - The authors use off-the-shelf siamese networks for their settting and thus it is questionable there is any novelty there. The application/setting may be novel, but not the architecture of choice. - From Eq. 4 to Eq. 5, the authors argue that the denominator does not depend on the model parameters and can be ignored. However, afterwards the objective does combine time-dependent discrimination indices of several competing risks, with different denominator values. This could be problematic if the risks are unbalanced. - The competitive gain of the authors method in comparison with other competing methods is minor. - The authors introduce F(t, D | x) as cumulative incidence function (CDF) at the beginning of section 2, however, afterwards they use R^m(t, x), which they define as risk of the subject experiencing event m before t. Is the latter a proxy for the former? How are they related?