Clustering Survival Data using a Mixture of Non-parametric Experts

TL;DR

This paper introduces SurvMixClust, a new algorithm that combines clustering with survival analysis using a mixture of non-parametric experts, improving cluster quality and predictive accuracy in survival prediction tasks.

Contribution

SurvMixClust is the first method to integrate clustering and survival function prediction in a unified non-parametric framework for survival analysis.

Findings

Creates balanced clusters with distinct survival curves

Outperforms baseline clustering methods in survival tasks

Achieves competitive predictive accuracy with non-clustering models

Abstract

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.