Evaluation of the Alzheimer’s Disease Progression Score

Alzheimer"s Disease;Statistical Evaluation;Machine Learning;Mathematics

Ye, ZhouJedynak, Bruno M. ;

Johns Hopkins University

关键词: Alzheimer";    s Disease;    Statistical Evaluation;    Machine Learning;    Mathematics;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/37106/YE-THESIS-2014.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

The Alzheimer’s Disease (AD) Progression Score (ADPS) method (Jedynak et al., Neuroimage 2012) was presented as an unsupervised statistical method which combines a heterogeneous collections of measurements of AD. It allows (1) the visualization of the cascade of mea- surements and (2) the assignment of an AD progression score (ADPS) to each subject and each time-point in a cohort. In this paper, we use the AD Neuroimaging Ini- tiative (ADNI) cohort to evaluate the ADPS using three standard benchmarks: (a) the prediction of the transition from Mild Cognitive Impairment (MCI) to AD dementia, (b) the sample size obtained for a simulated drug trial, and (c) the Cox proportional hazard model. We find that in all three benchmarks the ADPS method provides a progression score that is better than the best of the indi- vidual measurements available in ADNI.