论文标题
数据驱动的疾病进展模型
Data-Driven Disease Progression Modelling
论文作者
论文摘要
2010年之前，神经病学界的激烈争论最终达到了阿尔茨海默氏病进展的假设模型：生物标志物的病理生理级联，每一个动态仅用于整个疾病时间表的一部分。受到数据驱动的疾病进展建模的启发，来自计算机科学界出现，目的是使用来自大量患者，健康对照组和前驱/处于危险的人的数据来重建神经退行性疾病时间表。本章介绍了该领域的精选亮点，重点是理解和预测疾病进展的实用性。
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.
