Semi-Structured Deep Piecewise Exponential Models

MCML Authors

Christian Wachinger

Prof. Dr.

Principal Investigator

Artificial Intelligence in Medical Imaging

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Andreas Bender

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

David Rügamer

Prof. Dr.

Principal Investigator

Statistics, Data Science and Machine Learning

Abstract

We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise expo-nential models and thereby supports various survival tasks, such as competing risks and multi-state modeling, and further allows for estimation of time-varying effects and time-varying features. To also include multiple data sources and higher-order interaction effects into the model, we embed the model class in a neural network and thereby enable the si-multaneous estimation of both inherently interpretable structured regression inputs as well as deep neural network components which can potentially process additional unstructured data sources. A proof of concept is provided by using the framework to predict Alzheimer’s disease progression based on tabular and 3D point cloud data and applying it to synthetic data.

inproceedings

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AAAI-SPACA 2021

AAAI Spring Symposium Series on Survival Prediction: Algorithms, Challenges and Applications. Palo Alto, California, USA, Mar 21-24, 2021.

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Authors

P. Kopper • S. Pölsterl • C. Wachinger • B. Bischl • A. Bender • D. Rügamer

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Links

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Research Areas

 A1 | Statistical Foundations & Explainability

 C1 | Medicine