Differentiable sorting for censored time-to-event data

Survival analysis is a crucial semi-supervised task in machine learning with significant real-world applications, especially in healthcare. It is known that survival analysis can be reduced to a ranking task and be learnt with ordering supervision. Differentiable sorting methods have been shown to be effective in this area but are unable to handle censored orderings. To combat this, we propose Diffsurv, which predicts matrices of \emph{possible} permutations that accommodate the label uncertainty introduced by censored samples. Our experiments reveal that Diffsurv matches or outperforms established baselines in various semi-simulated and real-world risk prediction scenarios.