The TWIML AI podcast with Sam Charrington

Here's one to get started https://proceedings.mlr.press/v98/cotter19a.html

You can treat it is as a binary classification if you introduce an inverse probability weighting adjustment for censoring and certain assumptions are met. There's a big literature here and it's a bit difficult to pin down the single best reference. Here's a paper that discusses censoring-adjusted evaluation of these models using standard binary classification metrics (Blanche 2013 https://pubmed.ncbi.nlm.nih.gov/23794418/)