SurvSHAP(t): Time-dependent explanations of machine learning survival models. (arXiv:2208.11080v2 [cs.LG] UPDATED)

Machine and deep learning survival models demonstrate similar or even
improved time-to-event prediction capabilities compared to classical
statistical learning methods yet are too complex to be interpreted by humans.
Several model-agnostic explanations are available to overcome this issue;
however, none directly explain the survival function prediction. In this paper,
we introduce SurvSHAP(t), the first time-dependent explanation that allows for
interpreting survival black-box models. It is based on SHapley Additive
exPlanations with solid theoretical foundations and a broad adoption among
machine …

arxiv machine machine learning survival