mlsanityData to 14 Oct 2026

A Deep Variational Approach to Clustering Survival Data

arXiv 2106.05763 ↗First posted 10 Jun 2021cs.LG stat.MLcited by 25

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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.

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

20 of 97 references.

References · 20

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  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  4. 2017The Effectiveness of Data Augmentation in Image Classification using Deep Learningcited by 370
  5. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 358
  6. 2016Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoderscited by 220
  7. 2016Variational Deep Embedding: An Unsupervised and Generative Approach to Clusteringcited by 220
  8. 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 175
  9. 2016Composing graphical models with neural networks for structured representations and fast inferencecited by 113
  10. 2020Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Riskscited by 70
  11. 2016Deep Survival Analysiscited by 61
  12. 2021Deep Cox Mixtures for Survival Regressioncited by 44
8 more
  1. 2018Adversarial Time-to-Event Modelingcited by 33
  2. 2018Deep learning cardiac motion analysis for human survival predictioncited by 31
  3. 2021X-CAL: Explicit Calibration for Survival Analysiscited by 30
  4. 2020Survival Cluster Analysiscited by 17
  5. 2018Image-based Survival Analysis for Lung Cancer Patients using CNNscited by 11
  6. 2016Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scalecited by 10
  7. 2018Semi-crowdsourced Clustering with Deep Generative Modelscited by 7
  8. 2019Outcome-Driven Clustering of Acute Coronary Syndrome Patients using Multi-Task Neural Network with Attentioncited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 4; 2024: 8; 2025: 3; 2026: 6.

25 papers.

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  1. 2024On the Challenges and Opportunities in Generative AIcited by 31
  2. 2024An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomescited by 16
  3. 2022Counterfactual Phenotyping with Censored Time-to-Eventscited by 13
  4. 2022Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guaranteecited by 9
  5. 2024Intersectional Unfairness Discoverycited by 5
  6. 2023Deep Clustering Survival Machines with Interpretable Expert Distributionscited by 3
  7. 2024Clustering Survival Data using a Mixture of Non-parametric Expertscited by 3
  8. 2022Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Predictioncited by 2
  9. 2022Variable selection for nonlinear Cox regression model via deep learningcited by 2
  10. 2023A Prototype-Oriented Clustering for Domain Shift with Source Privacycited by 2
  11. 2023A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Informationcited by 2
  12. 2023Estimating treatment effects from single-arm trials via latent-variable modelingcited by 1
13 more
  1. 2024Feature Clock: High-Dimensional Effects in Two-Dimensional Plotscited by 1
  2. 2024SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal datacited by 1
  3. 2024MRI Parameter Mapping via Gaussian Mixture VAE: Breaking the Assumption of Independent Pixelscited by 1
  4. 2025Censor Dependent Variational Inferencecited by 1
  5. 2025Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Headscited by 1
  6. 2024Variational Deep Survival Machines: Survival Regression with Censored Outcomescited by 0
  7. 2025Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligencecited by 0
  8. 2026Deep Variational Contrastive Learning for Joint Risk Stratification and Time-to-Event Estimationcited by 0
  9. 2026Discovering Subgroups with Exceptional Survival Characteristicscited by 0
  10. 2026Functional Clustering of Survival Data via Smoothed Log-Hazard Trajectories: A Risk-Dynamics Perspectivecited by 0
  11. 2026Expert-Driven Survival Machines: Improving Stratification and Interpretability in Multiple Clinical Cohortscited by 0
  12. 2026iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysiscited by 0
  13. 2026K-Survival Meanscited by 0