Robust parametric modeling of Alzheimer's disease progression.

ABSTRACT: Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the robust modeling of Alzheimer's disease progression using parametric methods. The proposed method linearly maps the individual's age to a disease progression score (DPS) and jointly fits constrained generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS using M-estimation. Robustness of the estimates is quantified using bootstrapping via Monte Carlo resampling, and the estimated inflection points of the fitted functions are used to temporally order the modeled biomarkers in the disease course. Kernel density estimation is applied to the obtained DPSs for clinical status clas

SUBMITTER: Mehdipour Ghazi M 

PROVIDER: S-EPMC9068750 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature