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, it is applicable even if individual
biomarker measurements are missing for a subject without requiring a retraining of
the model. The functionality of the presented method is demonstrated using synthetic and
—employing cognitive scores and image-based biomarkers—real data from the ADNI
study. Further, three possible applications for progress estimation are demonstrated to
underline the versatility of the approach: classification, construction of a spatio-temporal disease
progression atlas and prediction of future disease progression.