# DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values

arXiv: https://arxiv.org/abs/1908.02337 · HTML: https://mlsanity.com/paper/1908.02337/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 15 of 15)

- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values](https://mlsanity.com/paper/2101.03170/) (2021)
- [FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis](https://mlsanity.com/paper/2207.05247/) (2022)
- [Optimal Sparse Survival Trees](https://mlsanity.com/paper/2401.15330/) (2024)
- [Interpretable Machine Learning for Survival Analysis](https://mlsanity.com/paper/2403.10250/) (2024)
- [Deep conditional transformation models for survival analysis](https://mlsanity.com/paper/2210.11366/) (2022)
- [Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model](https://mlsanity.com/paper/2405.11377/) (2024)
- [Survival Analysis as Imprecise Classification with Trainable Kernels](https://mlsanity.com/paper/2506.10140/) (2025)
- [Deep Neural Network Based Accelerated Failure Time Models using Rank Loss](https://mlsanity.com/paper/2206.05974/) (2022)
- [Unifying Gradients to Improve Real-world Robustness for Deep Networks](https://mlsanity.com/paper/2208.06228/) (2022)
- [BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect](https://mlsanity.com/paper/2211.10793/) (2022)
- [Two-step interpretable modeling of Intensive Care Acquired Infections](https://mlsanity.com/paper/2301.11146/) (2023)
- [Exploring Cumulative Effects in Survival Data Using Deep Learning Networks](https://mlsanity.com/paper/2512.23764/) (2025)
- [Dynamic Prediction of Alternating Recurrent Events via Neural Network](https://mlsanity.com/paper/2606.30889/) (2026)
- [Random Hazard Forests](https://mlsanity.com/paper/2608.21597/) (2026)

## Abstract (arXiv)

There has been increasing interest in modelling survival data using deep learning methods in medical research. Current approaches have focused on designing special cost functions to handle censored survival data. We propose a very different method with two steps. In the first step, we transform each subject's survival time into a series of jackknife pseudo conditional survival probabilities and then use these pseudo probabilities as a quantitative response variable in the deep neural network model. By using the pseudo values, we reduce a complex survival analysis to a standard regression problem, which greatly simplifies the neural network construction. Our two-step approach is simple, yet very flexible in making risk predictions for survival data, which is very appealing from the practice point of view. The source code is freely available at http://github.com/lilizhaoUM/DNNSurv.