A Deep Variational Approach to Clustering Survival Data

TL;DR

This paper introduces a deep probabilistic model for clustering survival data, effectively uncovering underlying distributions and subpopulations, with improved clustering and competitive survival prediction across diverse datasets.

Contribution

It presents a novel semi-supervised deep generative model leveraging variational inference for clustering survival data, addressing limitations of previous methods.

Findings

Outperforms existing clustering methods on synthetic and real datasets.

Achieves better cluster identification and competitive survival prediction.

Offers a holistic generative perspective on survival data analysis.

Abstract

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.