Clustering Survival Data using a Mixture of Non-parametric Experts
This addresses the underexplored integration of clustering into survival analysis for applications like precision medicine, representing an incremental advancement by combining existing concepts in a unified framework.
The paper tackles the problem of integrating clustering into survival analysis for precision medicine by introducing SurvMixClust, a novel algorithm that learns latent representations for clustering and predicts individual survival functions using a mixture of non-parametric experts. Results on five public datasets show it creates balanced clusters with distinct survival curves, outperforms clustering baselines and competes with non-clustering models in predictive accuracy as measured by time-dependent c-index and log-rank metrics.
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.