# Deep Learning for Survival Outcomes

arXiv: https://arxiv.org/abs/1904.10345 · HTML: https://mlsanity.com/paper/1904.10345/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data](https://mlsanity.com/paper/2110.14001/) (2021)
- [SurvNAM: The machine learning survival model explanation](https://mlsanity.com/paper/2104.08903/) (2021)
- [Inverse-Weighted Survival Games](https://mlsanity.com/paper/2111.08175/) (2021)
- [Neural interval-censored survival regression with feature selection](https://mlsanity.com/paper/2206.06885/) (2022)
- [A Statistical Learning Take on the Concordance Index for Survival Analysis](https://mlsanity.com/paper/2302.12059/) (2023)
- [Survival Concept-Based Learning Models](https://mlsanity.com/paper/2502.05950/) (2025)
- [SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis](https://mlsanity.com/paper/2605.22776/) (2026)

## Abstract (arXiv)

This manuscripts develops a new class of deep learning algorithms for outcomes that are potentially censored. To account for censoring, the unobservable loss function used in the absence of censoring is replaced by a censoring unbiased transformation. The resulting class of algorithms can be used to estimate both survival probabilities and restricted mean survival. We show how the deep learning algorithms can be implemented using software for uncensored data using a form of response transformation. Simulations and analysis of the Netherlands 70 Gene Signature Data show strong performance of the proposed algorithms.