# Deep Learning for Survival Outcomes

## TL;DR

This paper introduces a novel deep learning framework tailored for censored survival data, enabling accurate estimation of survival probabilities and mean survival times, with demonstrated strong performance on simulated and real datasets.

## Contribution

It develops a new class of deep learning algorithms that handle censored outcomes by using a censoring unbiased transformation, extending deep learning methods to survival analysis.

## Key findings

- Strong performance in simulations
- Effective estimation of survival probabilities
- Accurate restricted mean survival estimates

## Abstract

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.