Energy-based survival modelling using harmoniums

TL;DR

This paper introduces an energy-based harmonium model for survival analysis that effectively handles multiple censored time-to-event variables and missing data, capturing complex patterns and improving predictive accuracy.

Contribution

The paper presents a novel harmonium-based approach for multi-event survival analysis, integrating censored and missing data within a unified energy-based framework.

Findings

Model captures non-linear patterns in survival data.

Performs comparably to established methods on single-event data.

Improves predictions by leveraging multiple event variables.

Abstract

Survival analysis concerns the study of timeline data where the event of interest may remain unobserved (i.e., censored). Studies commonly record more than one type of event, but conventional survival techniques focus on a single event type. We set out to integrate both multiple independently censored time-to-event variables as well as missing observations. An energy-based approach is taken with a bi-partite structure between latent and visible states, known as harmoniums (or restricted Boltzmann machines). The present harmonium is shown, both theoretically and experimentally, to capture non-linearly separable patterns between distinct time recordings. We illustrate on real world data that, for a single time-to-event variable, our model is on par with established methods. In addition, we demonstrate that discriminative predictions improve by leveraging an extra time-to-event variable. In conclusion, multiple time-to-event variables can be successfully captured within the harmonium paradigm.