Just in case you missed it, https://en.m.wikipedia.org/wiki/Survival_analysis https://en.m.wikipedia.org/wiki/Survival_analysis exists to answer specifically th

Just in case you missed it, https://en.m.wikipedia.org/wiki/Survival_analysis https://en.m.wikipedia.org/wiki/Survival_analysis exists to answer specifically this question. In more practical terms, if I were to approach this problem, I'd discretize it in time and apply classical ml to predict "chance to die during month X assuming you survived that long" and fit it to data - that'd be much easier to spot errors and potential issues with your data. I'd go for the stochastic calculus or actual survival analysis only if you wanted to prove/draw a connection between some pre-existing mathematical properly such as memory-less-ness and a physical/biological properly of a system such as behavior of certain proteins (that'd be insanely cool, but rather hard, esp if data is limited). In my (very vague) understanding, that's what finance papers that use stochastic analysis do - they make a mathematical assumption about some universal mathematical properly of a system (if markets were always near optimal with probability of deviation decaying as XYZ, the world economy would react this way to these things), and then prove that it actually fits the data. Happy to chat more, sounds like a fun project :)