Name
Title | ||
|---|---|---|
A Deep Latent-Variable Model Application to Select Treatment Intensity in Survival Analysis. |
Abstract | ||
|---|---|---|
In the following short article we adapt a new and popular machine learning model for inference on medical data sets. Our method is based on the Variational AutoEncoder (VAE) framework that we adapt to survival analysis on small data sets with missing values. In our model, the true health status appears as a set of latent variables that affects the observed covariates and the survival chances. We show that this flexible model allows insightful decision-making using a predicted distribution and outperforms a classic survival analysis model. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Machine Learning | Journal |
Volume | Citations | PageRank |
abs/1811.12323 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cédric Beaulac | 1 | 0 | 1.01 |
| Jeffrey S. Rosenthal | 2 | 357 | 43.06 |
| David Hodgson | 3 | 7 | 1.45 |