# Survival Cluster Analysis

arXiv: https://arxiv.org/abs/2003.00355 · HTML: https://mlsanity.com/paper/2003.00355/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2020.

## Built on by (most cited first, 17 of 17)

- [Deep Cox Mixtures for Survival Regression](https://mlsanity.com/paper/2101.06536/) (2021)
- [A Deep Variational Approach to Clustering Survival Data](https://mlsanity.com/paper/2106.05763/) (2021)
- [An Effective Meaningful Way to Evaluate Survival Models](https://mlsanity.com/paper/2306.01196/) (2023)
- [Counterfactual Phenotyping with Censored Time-to-Events](https://mlsanity.com/paper/2202.11089/) (2022)
- [Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration](https://mlsanity.com/paper/2405.07374/) (2024)
- [Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee](https://mlsanity.com/paper/2206.10477/) (2022)
- [Deep Clustering Survival Machines with Interpretable Expert Distributions](https://mlsanity.com/paper/2301.11826/) (2023)
- [Clustering Survival Data using a Mixture of Non-parametric Experts](https://mlsanity.com/paper/2405.15934/) (2024)
- [A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Information](https://mlsanity.com/paper/2305.06862/) (2023)
- [Censor Dependent Variational Inference](https://mlsanity.com/paper/2502.09591/) (2025)
- [Infinite hierarchical contrastive clustering for personal digital envirotyping](https://mlsanity.com/paper/2505.15022/) (2025)
- [Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence](https://mlsanity.com/paper/2506.12944/) (2025)
- [Deep Variational Contrastive Learning for Joint Risk Stratification and Time-to-Event Estimation](https://mlsanity.com/paper/2602.01367/) (2026)
- [Functional Clustering of Survival Data via Smoothed Log-Hazard Trajectories: A Risk-Dynamics Perspective](https://mlsanity.com/paper/2606.01239/) (2026)
- [Expert-Driven Survival Machines: Improving Stratification and Interpretability in Multiple Clinical Cohorts](https://mlsanity.com/paper/2606.14608/) (2026)
- [iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis](https://mlsanity.com/paper/2607.08778/) (2026)
- [K-Survival Means](https://mlsanity.com/paper/2607.24405/) (2026)

## Abstract (arXiv)

Conventional survival analysis approaches estimate risk scores or individualized time-to-event distributions conditioned on covariates. In practice, there is often great population-level phenotypic heterogeneity, resulting from (unknown) subpopulations with diverse risk profiles or survival distributions. As a result, there is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles, while jointly accounting for accurate individualized time-to-event predictions. An approach that addresses this need is likely to improve characterization of individual outcomes by leveraging regularities in subpopulations, thus accounting for population-level heterogeneity. In this paper, we propose a Bayesian nonparametrics approach that represents observations (subjects) in a clustered latent space, and encourages accurate time-to-event predictions and clusters (subpopulations) with distinct risk profiles. Experiments on real-world datasets show consistent improvements in predictive performance and interpretability relative to existing state-of-the-art survival analysis models.