• Corpus ID: 239050076

Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation

@article{Weber2021TowardsMH,
  title={Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation},
  author={Tobias Weber and Michael Ingrisch and Bernd Bischl and David R{\"u}gamer},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.11312}
}
The application of deep learning in survival analysis (SA) allows utilizing 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… 

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