# A Deep Variational Approach to Clustering Survival Data

arXiv: https://arxiv.org/abs/2106.05763 · HTML: https://mlsanity.com/paper/2106.05763/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 97 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 20 of 20)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/1611.02648/) (2016)
- [Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering](https://mlsanity.com/paper/1611.05148/) (2016)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Composing graphical models with neural networks for structured representations and fast inference](https://mlsanity.com/paper/1603.06277/) (2016)
- [Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks](https://mlsanity.com/paper/2003.01176/) (2020)
- [Deep Survival Analysis](https://mlsanity.com/paper/1608.02158/) (2016)
- [Deep Cox Mixtures for Survival Regression](https://mlsanity.com/paper/2101.06536/) (2021)
- [Adversarial Time-to-Event Modeling](https://mlsanity.com/paper/1804.03184/) (2018)
- [Deep learning cardiac motion analysis for human survival prediction](https://mlsanity.com/paper/1810.03382/) (2018)
- [X-CAL: Explicit Calibration for Survival Analysis](https://mlsanity.com/paper/2101.05346/) (2021)
- [Survival Cluster Analysis](https://mlsanity.com/paper/2003.00355/) (2020)
- [Image-based Survival Analysis for Lung Cancer Patients using CNNs](https://mlsanity.com/paper/1808.09679/) (2018)
- [Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale](https://mlsanity.com/paper/1605.05797/) (2016)
- [Semi-crowdsourced Clustering with Deep Generative Models](https://mlsanity.com/paper/1810.11971/) (2018)
- [Outcome-Driven Clustering of Acute Coronary Syndrome Patients using Multi-Task Neural Network with Attention](https://mlsanity.com/paper/1903.00197/) (2019)

## Built on by (most cited first, 25 of 25)

- [On the Challenges and Opportunities in Generative AI](https://mlsanity.com/paper/2403.00025/) (2024)
- [An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes](https://mlsanity.com/paper/2410.01086/) (2024)
- [Counterfactual Phenotyping with Censored Time-to-Events](https://mlsanity.com/paper/2202.11089/) (2022)
- [Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee](https://mlsanity.com/paper/2206.10477/) (2022)
- [Intersectional Unfairness Discovery](https://mlsanity.com/paper/2405.20790/) (2024)
- [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)
- [Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction](https://mlsanity.com/paper/2209.15450/) (2022)
- [Variable selection for nonlinear Cox regression model via deep learning](https://mlsanity.com/paper/2211.09287/) (2022)
- [A Prototype-Oriented Clustering for Domain Shift with Source Privacy](https://mlsanity.com/paper/2302.03807/) (2023)
- [A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Information](https://mlsanity.com/paper/2305.06862/) (2023)
- [Estimating treatment effects from single-arm trials via latent-variable modeling](https://mlsanity.com/paper/2311.03002/) (2023)
- [Feature Clock: High-Dimensional Effects in Two-Dimensional Plots](https://mlsanity.com/paper/2408.01294/) (2024)
- [SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal data](https://mlsanity.com/paper/2409.12709/) (2024)
- [MRI Parameter Mapping via Gaussian Mixture VAE: Breaking the Assumption of Independent Pixels](https://mlsanity.com/paper/2411.10772/) (2024)
- [Censor Dependent Variational Inference](https://mlsanity.com/paper/2502.09591/) (2025)
- [Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Heads](https://mlsanity.com/paper/2511.09567/) (2025)
- [Variational Deep Survival Machines: Survival Regression with Censored Outcomes](https://mlsanity.com/paper/2404.15595/) (2024)
- [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)
- [Discovering Subgroups with Exceptional Survival Characteristics](https://mlsanity.com/paper/2602.22179/) (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)

In this work, we study the problem of clustering survival data - a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference. In contrast to previous work, our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and censored survival times. We compare our model to the related work on clustering and mixture models for survival data in comprehensive experiments on a wide range of synthetic, semi-synthetic, and real-world datasets, including medical imaging data. Our method performs better at identifying clusters and is competitive at predicting survival times. Relying on novel generative assumptions, the proposed model offers a holistic perspective on clustering survival data and holds a promise of discovering subpopulations whose survival is regulated by different generative mechanisms.