Data-Driven Disease Progression Modelling

TL;DR

This paper reviews data-driven models of neurodegenerative disease progression, especially Alzheimer's, highlighting how large datasets enable reconstruction and forecasting of disease timelines to improve understanding and prediction.

Contribution

It summarizes recent advances in data-driven disease progression modeling, emphasizing methods for reconstructing disease timelines from large cohort data.

Findings

Data-driven models effectively reconstruct disease timelines.

Models improve forecasting of disease progression.

Large cohort data enhances model accuracy.

Abstract

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.