Learning Biomarker Models for Progression Estimation of Alzheimer's Disease.

ABSTRACT: Being able to estimate a patient's progress in the course of Alzheimer's disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, i

SUBMITTER: Schmidt-Richberg A 

PROVIDER: S-EPMC4838309 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature