# Data-Driven Disease Progression Modelling

arXiv: https://arxiv.org/abs/2211.05786 · HTML: https://mlsanity.com/paper/2211.05786/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 60 references that are in the map.
- First posted on arXiv 1 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Clustering Interval-Censored Time-Series for Disease Phenotyping](https://mlsanity.com/paper/2102.07005/) (2021)
- [DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders](https://mlsanity.com/paper/1901.03553/) (2019)
- [Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling](https://mlsanity.com/paper/1808.03604/) (2018)
- [Ten years of image analysis and machine learning competitions in dementia](https://mlsanity.com/paper/2112.07922/) (2021)
- [A Discriminative Event Based Model for Alzheimer's Disease Progression Modeling](https://mlsanity.com/paper/1702.06408/) (2017)
- [A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging](https://mlsanity.com/paper/1604.00912/) (2016)
- [Bayesian latent time joint mixed effect models for multicohort longitudinal data](https://mlsanity.com/paper/1703.10266/) (2017)
- [Learning transition times in event sequences: the Event-Based Hidden Markov Model of disease progression](https://mlsanity.com/paper/2011.01023/) (2020)

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

- [Evaluation of the impact of expert knowledge: How decision support scores impact the effectiveness of automatic knowledge-driven feature engineering (aKDFE)](https://mlsanity.com/paper/2504.05928/) (2025)

## Abstract (arXiv)

Intense debate in the Neurology community before 2010 culminated in hypothetical models of Alzheimer's disease progression: a pathophysiological cascade of biomarkers, each dynamic for only a segment of the full disease timeline. Inspired by this, data-driven disease progression modelling emerged from the computer science community with the aim to reconstruct neurodegenerative disease timelines using data from large cohorts of patients, healthy controls, and prodromal/at-risk individuals. This chapter describes selected highlights from the field, with a focus on utility for understanding and forecasting of disease progression.