Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction. (arXiv:2209.15450v1 [cs.LG])

Interpreting critical variables involved in complex biological processes
related to survival time can help understand prediction from survival models,
evaluate treatment efficacy, and develop new therapies for patients. Currently,
the predictive results of deep learning (DL)-based models are better than or as
good as standard survival methods, they are often disregarded because of their
lack of transparency and little interpretability, which is crucial to their
adoption in clinical applications. In this paper, we introduce a novel, easily
deployable approach, called …

arxiv features prediction survival values