# Neural interval-censored survival regression with feature selection

arXiv: https://arxiv.org/abs/2206.06885 · HTML: https://mlsanity.com/paper/2206.06885/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [LassoNet: A Neural Network with Feature Sparsity](https://mlsanity.com/paper/1907.12207/) (2019)
- [ResNet with one-neuron hidden layers is a Universal Approximator](https://mlsanity.com/paper/1806.10909/) (2018)
- [CD-split and HPD-split: efficient conformal regions in high dimensions](https://mlsanity.com/paper/2007.12778/) (2020)
- [Survival Regression with Proper Scoring Rules and Monotonic Neural Networks](https://mlsanity.com/paper/2103.14755/) (2021)
- [High-Dimensional Inference: Confidence Intervals, p-Values and R-Software hdi](https://mlsanity.com/paper/1408.4026/) (2014)
- [Distributional data analysis of accelerometer data from the NHANES database using nonparametric survey regression models](https://mlsanity.com/paper/2104.01165/) (2021)
- [Deep Learning for Survival Outcomes](https://mlsanity.com/paper/1904.10345/) (2019)
- [An Ensemble Method for Interval-Censored Time-to-Event Data](https://mlsanity.com/paper/1901.04599/) (2019)
- [Interval censored recursive forests](https://mlsanity.com/paper/1912.09983/) (2019)
- [Model Based Bootstrap Methods for Interval Censored Data](https://mlsanity.com/paper/1312.6341/) (2013)

## Built on by (most cited first, 1 of 1)

- [ICODEN: Ordinary Differential Equation Neural Networks for Interval-Censored Data](https://mlsanity.com/paper/2602.10303/) (2026)

## Abstract (arXiv)

Survival analysis is a fundamental area of focus in biomedical research, particularly in the context of personalized medicine. This prominence is due to the increasing prevalence of large and high-dimensional datasets, such as omics and medical image data. However, the literature on non-linear regression algorithms and variable selection techniques for interval-censoring is either limited or non-existent, particularly in the context of neural networks. Our objective is to introduce a novel predictive framework tailored for interval-censored regression tasks, rooted in Accelerated Failure Time (AFT) models. Our strategy comprises two key components: i) a variable selection phase leveraging recent advances on sparse neural network architectures, ii) a regression model targeting prediction of the interval-censored response. To assess the performance of our novel algorithm, we conducted a comprehensive evaluation through both numerical experiments and real-world applications that encompass scenarios related to diabetes and physical activity. Our results outperform traditional AFT algorithms, particularly in scenarios featuring non-linear relationships.