Energy-based survival modelling using harmoniums

• Main Authors: Donker, Hylke C, Groen, Harry J. M
• Format: Journal Article
• Language: English
• Published: 05-10-2021
• Subjects: Computer Science - Learning; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2110.01960

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.