# Deep learning for comprehensive forecasting of Alzheimer's Disease progression

arXiv: https://arxiv.org/abs/1807.03876 · HTML: https://mlsanity.com/paper/1807.03876/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2018.

## Built on by (most cited first, 11 of 11)

- [REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers](https://mlsanity.com/paper/2302.02041/) (2023)
- [Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review](https://mlsanity.com/paper/2108.04174/) (2021)
- [TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography](https://mlsanity.com/paper/2210.07411/) (2022)
- [Neural Boltzmann Machines](https://mlsanity.com/paper/2305.08337/) (2023)
- [Methods for generating and evaluating synthetic longitudinal patient data: a systematic review](https://mlsanity.com/paper/2309.12380/) (2023)
- [Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data](https://mlsanity.com/paper/2606.09671/) (2026)
- [Interpretable machine learning for high-dimensional trajectories of aging health](https://mlsanity.com/paper/2105.03410/) (2021)
- [A Generalized Variable Importance Metric and Estimator for Black Box Machine Learning Models](https://mlsanity.com/paper/2212.09931/) (2022)
- [Graph Theory and GNNs to Unravel the Topographical Organization of Brain Lesions in Variants of Alzheimer's Disease Progression](https://mlsanity.com/paper/2403.00636/) (2024)
- [CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease](https://mlsanity.com/paper/2604.22428/) (2026)
- [A Patient World Model for Early Forecasting of Digital Health Campaign Outcomes: Capabilities and Limits](https://mlsanity.com/paper/2609.23333/) (2026)

## Abstract (arXiv)

Most approaches to machine learning from electronic health data can only predict a single endpoint. Here, we present an alternative that uses unsupervised deep learning to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1908 patients with Mild Cognitive Impairment or Alzheimer's Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics, generating both predictions and their confidence intervals. Our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models and identifies sub-components associated with word recall as predictive of progression. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer's Disease.