A Deep Learning Approach for Survival Clustering without End-of-life Signals

Abstract

The goal of survival clustering is to map subjects (e.g., users in a social network, patients in a medical study) to K clusters ranging from low-risk to high-risk. Existing survival methods assume the presence of clear end-of-life signals or introduce them artificially using a pre-defined timeout. In this paper, we forego this assumption and introduce a loss function that differentiates between the empirical lifetime distributions of the clusters using a modified Kuiper statistic. We learn a deep neural network by optimizing this loss, that performs a soft clustering of users into survival groups. We apply our method to a social network dataset with over 1M subjects, and show significant improvement in C-index compared to alternatives.

Tasks

Reproductions