# A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

arXiv: https://arxiv.org/abs/1909.03890 · HTML: https://mlsanity.com/paper/1909.03890/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 12 of 12)

- [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)
- [Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review](https://mlsanity.com/paper/2108.04174/) (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)
- [Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany](https://mlsanity.com/paper/2101.00661/) (2021)
- [Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis](https://mlsanity.com/paper/2205.01188/) (2022)
- [Interpretable Machine Learning for Survival Analysis](https://mlsanity.com/paper/2403.10250/) (2024)
- [Bayesian Semi-structured Subspace Inference](https://mlsanity.com/paper/2401.12950/) (2024)
- [Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data](https://mlsanity.com/paper/2107.05997/) (2021)
- [KNN, An Underestimated Model for Regional Rainfall Forecasting](https://mlsanity.com/paper/2103.15235/) (2021)
- [Controlling for Omitted Variable Bias in Deep Neural Networks](https://mlsanity.com/paper/2608.25930/) (2026)

## Abstract (arXiv)

We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations and avoids the need to identify point correspondences between shapes. To account for right censored time-to-event data, i.e., when it is only known that a patient did not develop Alzheimer's disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer's Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.