# Semi-Structured Deep Piecewise Exponential Models

arXiv: https://arxiv.org/abs/2011.05824 · HTML: https://mlsanity.com/paper/2011.05824/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 13 of 13)

- [MOTOR: A Time-To-Event Foundation Model For Structured Medical Records](https://mlsanity.com/paper/2301.03150/) (2023)
- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform](https://mlsanity.com/paper/2107.05990/) (2021)
- [deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression](https://mlsanity.com/paper/2104.02705/) (2021)
- [DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis](https://mlsanity.com/paper/2202.07423/) (2022)
- [A New PHO-rmula for Improved Performance of Semi-Structured Networks](https://mlsanity.com/paper/2306.00522/) (2023)
- [Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data](https://mlsanity.com/paper/2107.05997/) (2021)
- [Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation](https://mlsanity.com/paper/2110.11312/) (2021)
- [Additive Higher-Order Factorization Machines](https://mlsanity.com/paper/2205.14515/) (2022)
- [Survival-oriented embeddings for improving accessibility to complex data structures](https://mlsanity.com/paper/2110.11303/) (2021)
- [Generalizing Orthogonalization for Models with Non-Linearities](https://mlsanity.com/paper/2405.02475/) (2024)
- [A Functional Extension of Semi-Structured Networks](https://mlsanity.com/paper/2410.05430/) (2024)
- [A renormalization-group inspired lattice-based framework for piecewise generalized linear models](https://mlsanity.com/paper/2605.05493/) (2026)

## Abstract (arXiv)

We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise exponential 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 simultaneous 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.