Towards modelling hazard factors in unstructured data spaces using
gradient-based latent interpolation
- URL: http://arxiv.org/abs/2110.11312v1
- Date: Thu, 21 Oct 2021 17:46:03 GMT
- Title: Towards modelling hazard factors in unstructured data spaces using
gradient-based latent interpolation
- Authors: Tobias Weber, Michael Ingrisch, Bernd Bischl, David R\"ugamer
- Abstract summary: The application of deep learning in survival analysis (SA) gives the opportunity to utilize unstructured and high-dimensional data types uncommon in traditional survival methods.
This allows to advance methods in fields such as digital health, predictive maintenance and churn analysis.
We propose 1) a multi-task variational autoencoder (VAE) with survival objective, yielding survival-oriented embeddings, and 2) a novel method HazardWalk that allows to model hazard factors in the original data space.
- Score: 2.3867305921818573
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The application of deep learning in survival analysis (SA) gives the
opportunity to utilize unstructured and high-dimensional data types uncommon in
traditional survival methods. This allows to advance methods in fields such as
digital health, predictive maintenance and churn analysis, but often yields
less interpretable and intuitively understandable models due to the black-box
character of deep learning-based approaches. We close this gap by proposing 1)
a multi-task variational autoencoder (VAE) with survival objective, yielding
survival-oriented embeddings, and 2) a novel method HazardWalk that allows to
model hazard factors in the original data space. HazardWalk transforms the
latent distribution of our autoencoder into areas of maximized/minimized hazard
and then uses the decoder to project changes to the original domain. Our
procedure is evaluated on a simulated dataset as well as on a dataset of CT
imaging data of patients with liver metastases.
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