# Predicting Alzheimers Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort

arXiv: https://arxiv.org/abs/2306.10326 · HTML: https://mlsanity.com/paper/2306.10326/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 17 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis](https://mlsanity.com/paper/2205.01188/) (2022)

## Built on by (most cited first, 1 of 1)

- [Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling](https://mlsanity.com/paper/2409.16231/) (2024)

## Abstract (arXiv)

The rise of Alzheimers Disease worldwide has prompted a search for efficient tools which can be used to predict deterioration in cognitive decline leading to dementia. In this paper, we explore the potential of survival machine learning as such a tool for building models capable of predicting not only deterioration but also the likely time to deterioration. We demonstrate good predictive ability (0.86 C-Index), lending support to its use in clinical investigation and prediction of Alzheimers Disease risk.