# Clustering Survival Data using a Mixture of Non-parametric Experts

arXiv: https://arxiv.org/abs/2405.15934 · HTML: https://mlsanity.com/paper/2405.15934/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 8 of 8)

- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Machine Learning for Survival Analysis: A Survey](https://mlsanity.com/paper/1708.04649/) (2017)
- [Continuous and Discrete-Time Survival Prediction with Neural Networks](https://mlsanity.com/paper/1910.06724/) (2019)
- [A Deep Variational Approach to Clustering Survival Data](https://mlsanity.com/paper/2106.05763/) (2021)
- [Survival Cluster Analysis](https://mlsanity.com/paper/2003.00355/) (2020)
- [A weighted random survival forest](https://mlsanity.com/paper/1901.00213/) (2019)
- [Deep Lifetime Clustering](https://mlsanity.com/paper/1910.00547/) (2019)
- [Finite mixture modeling of censored and missing data using the multivariate skew-normal distribution](https://mlsanity.com/paper/2009.10826/) (2020)

## Built on by (most cited first, 3 of 3)

- [Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Heads](https://mlsanity.com/paper/2511.09567/) (2025)
- [Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence](https://mlsanity.com/paper/2506.12944/) (2025)
- [Functional Clustering of Survival Data via Smoothed Log-Hazard Trajectories: A Risk-Dynamics Perspective](https://mlsanity.com/paper/2606.01239/) (2026)

## Abstract (arXiv)

Survival analysis aims to predict the timing of future events across various fields, from medical outcomes to customer churn. However, the integration of clustering into survival analysis, particularly for precision medicine, remains underexplored. This study introduces SurvMixClust, a novel algorithm for survival analysis that integrates clustering with survival function prediction within a unified framework. SurvMixClust learns latent representations for clustering while also predicting individual survival functions using a mixture of non-parametric experts. Our evaluations on five public datasets show that SurvMixClust creates balanced clusters with distinct survival curves, outperforms clustering baselines, and competes with non-clustering survival models in predictive accuracy, as measured by the time-dependent c-index and log-rank metrics.