# Energy-based survival modelling using harmoniums

arXiv: https://arxiv.org/abs/2110.01960 · HTML: https://mlsanity.com/paper/2110.01960/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 48 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [A Scalable Discrete-Time Survival Model for Neural Networks](https://mlsanity.com/paper/1805.00917/) (2018)
- [Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image Statistics](https://mlsanity.com/paper/1401.5900/) (2014)
- [Thurstonian Boltzmann Machines: Learning from Multiple Inequalities](https://mlsanity.com/paper/1408.0055/) (2014)

## Built on by (most cited first, 1 of 1)

- [Multinomial belief networks for healthcare data](https://mlsanity.com/paper/2311.16909/) (2023)

## Abstract (arXiv)

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.