# High-Dimensional Survival Analysis: Methods and Applications

arXiv: https://arxiv.org/abs/2205.02948 · HTML: https://mlsanity.com/paper/2205.02948/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2022.

## Built on by (most cited first, 16 of 16)

- [Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction](https://mlsanity.com/paper/2401.01646/) (2024)
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- [Generating Survival Interpretable Trajectories and Data](https://mlsanity.com/paper/2402.12331/) (2024)
- [Generating Accurate Synthetic Survival Data by Conditioning on Outcomes](https://mlsanity.com/paper/2405.17333/) (2024)
- [Survival Analysis as Imprecise Classification with Trainable Kernels](https://mlsanity.com/paper/2506.10140/) (2025)
- [Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission](https://mlsanity.com/paper/2403.06999/) (2024)
- [SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms](https://mlsanity.com/paper/2412.07638/) (2024)
- [FinSurvival: A Suite of Large Scale Survival Modeling Tasks from Finance](https://mlsanity.com/paper/2507.14160/) (2025)
- [Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm](https://mlsanity.com/paper/2212.12028/) (2022)
- [Survival Analysis with Adversarial Regularization](https://mlsanity.com/paper/2312.16019/) (2023)
- [Realtime, multimodal invasive ventilation risk monitoring using language models and BoXHED](https://mlsanity.com/paper/2410.03725/) (2024)
- [Survival Concept-Based Learning Models](https://mlsanity.com/paper/2502.05950/) (2025)
- [TabSurv: Adapting Modern Tabular Neural Networks to Survival Analysis](https://mlsanity.com/paper/2605.03944/) (2026)
- [SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis](https://mlsanity.com/paper/2605.22776/) (2026)
- [Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy](https://mlsanity.com/paper/2605.25050/) (2026)
- [Non-Crossing Deep Quantile Regression for Distributional Survival Prediction](https://mlsanity.com/paper/2608.16864/) (2026)

## Abstract (arXiv)

In the era of precision medicine, time-to-event outcomes such as time to death or progression are routinely collected, along with high-throughput covariates. These high-dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability due to over-fitting. To overcome this, recent emphasis has been placed on developing novel approaches for feature selection and survival prognostication. We will review various cutting-edge methods that handle survival outcome data with high-dimensional predictors, highlighting recent innovations in machine learning approaches for survival prediction. We will cover the statistical intuitions and principles behind these methods and conclude with extensions to more complex settings, where competing events are observed. We exemplify these methods with applications to the Boston Lung Cancer Survival Cohort study, one of the largest cancer epidemiology cohorts investigating the complex mechanisms of lung cancer.