Data-Driven Disease Progression Modelling
Not on the map. Why?
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 60 references that are in the map. First posted on arXiv 1 Nov 2022.
Builds on
8 of 60 references.
References · 8
- 2021Clustering Interval-Censored Time-Series for Disease Phenotypingcited by 11
- 2019DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorderscited by 6
- 2018Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modelingcited by 5
- 2021Ten years of image analysis and machine learning competitions in dementiacited by 5
- 2017A Discriminative Event Based Model for Alzheimer's Disease Progression Modelingcited by 3
- 2016A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imagingcited by 2
- 2017Bayesian latent time joint mixed effect models for multicohort longitudinal datacited by 2
- 2020Learning transition times in event sequences: the Event-Based Hidden Markov Model of disease progressioncited by 1
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Citing papers per year: 2025: 1.
1 paper.