Energy-based survival modelling using harmoniums
This addresses the limitation of conventional survival techniques that focus on single events, offering a method for multi-event scenarios, though it appears incremental as it builds on existing harmonium paradigms.
The paper tackled the problem of modeling multiple censored time-to-event variables in survival analysis, showing that their energy-based harmonium model captures non-linearly separable patterns and improves discriminative predictions by leveraging an extra variable.
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.