Learning Survival Distribution with Implicit Survival Function
Abstract
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.
Cite
Text
Ling et al. "Learning Survival Distribution with Implicit Survival Function." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/442Markdown
[Ling et al. "Learning Survival Distribution with Implicit Survival Function." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/ling2023ijcai-learning/) doi:10.24963/IJCAI.2023/442BibTeX
@inproceedings{ling2023ijcai-learning,
title = {{Learning Survival Distribution with Implicit Survival Function}},
author = {Ling, Yu and Tan, Weimin and Yan, Bo},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {3975-3983},
doi = {10.24963/IJCAI.2023/442},
url = {https://mlanthology.org/ijcai/2023/ling2023ijcai-learning/}
}