Survival Mixture Density Networks. (arXiv:2208.10759v1 [cs.LG])

Survival analysis, the art of time-to-event modeling, plays an important role
in clinical treatment decisions. Recently, continuous time models built from
neural ODEs have been proposed for survival analysis. However, the training of
neural ODEs is slow due to the high computational complexity of neural ODE
solvers. Here, we propose an efficient alternative for flexible continuous time
models, called Survival Mixture Density Networks (Survival MDNs). Survival MDN
applies an invertible positive function to the output of Mixture Density
Networks (MDNs). …

arxiv lg networks survival