ICTSurF: Implicit Continuous-Time Survival Functions with Neural Networks
This work addresses limitations in survival analysis for applications like healthcare by proposing a novel method that avoids strong assumptions of traditional models, though it appears incremental in the context of deep learning-based approaches.
The paper tackles the challenge of censored samples in survival analysis by introducing ICTSurF, an implicit continuous-time survival function using neural networks, which achieves competitive performance compared to existing methods.
Survival analysis is a widely known method for predicting the likelihood of an event over time. The challenge of dealing with censored samples still remains. Traditional methods, such as the Cox Proportional Hazards (CPH) model, hinge on the limitations due to the strong assumptions of proportional hazards and the predetermined relationships between covariates. The rise of models based on deep neural networks (DNNs) has demonstrated enhanced effectiveness in survival analysis. This research introduces the Implicit Continuous-Time Survival Function (ICTSurF), built on a continuous-time survival model, and constructs survival distribution through implicit representation. As a result, our method is capable of accepting inputs in continuous-time space and producing survival probabilities in continuous-time space, independent of neural network architecture. Comparative assessments with existing methods underscore the high competitiveness of our proposed approach. Our implementation of ICTSurF is available at https://github.com/44REAM/ICTSurF.