Learning Survival Distribution with Implicit Survival Function
This addresses the need for more flexible and generalizable survival analysis methods in fields like healthcare and finance, though it is incremental as it builds on existing neural representation techniques.
The paper tackled the problem of modeling survival distributions with censored data by proposing an Implicit Survival Function (ISF) based on Implicit Neural Representation, which avoids strong assumptions and outperforms state-of-the-art methods on three public datasets with robustness to hyperparameters.
Survival analysis aims at modeling the relationship between covariates and event occurrence with some untracked (censored) samples. In implementation, existing methods model the survival distribution with strong assumptions or in a discrete time space for likelihood estimation with censorship, which leads to weak generalization. In this paper, we propose Implicit Survival Function (ISF) based on Implicit Neural Representation for survival distribution estimation without strong assumptions,and employ numerical integration to approximate the cumulative distribution function for prediction and optimization. Experimental results show that ISF outperforms the state-of-the-art methods in three public datasets and has robustness to the hyperparameter controlling estimation precision.