Data-Driven Disease Progression Modelling
This work provides a review of methods for modelling neurodegenerative diseases like Alzheimer's, which is incremental as it summarizes existing research.
The chapter reviews data-driven disease progression modelling, which reconstructs neurodegenerative disease timelines using patient cohort data to understand and forecast disease progression.
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.