Towards Modelling Hazard Factors in Unstructured Data Spaces Using Gradient-Based Latent Interpolation

MCML Authors

Tobias Weber

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Michael Ingrisch

Prof. Dr.

Principal Investigator

Clinical Data Science in Radiology

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

David Rügamer

Prof. Dr.

Principal Investigator

Statistics, Data Science and Machine Learning

Abstract

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 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.

inproceedings

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Deep Generative Models and Downstream Applications @NeurIPS 2021

Workshop on Deep Generative Models and Downstream Applications at the 35th Conference on Neural Information Processing Systems. Virtual, Dec 06-14, 2021.

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Authors

T. Weber • M. Ingrisch • B. Bischl • D. Rügamer

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Links

PDF

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Research Areas

 A1 | Statistical Foundations & Explainability

 C1 | Medicine