Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease
Molina Abril, Helena
Frangi, Alejandro F.
|Department||Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII)|
|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 ...
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.
|Funding agencies||European Commission (EC)|
|Citation||Schmidt-Richberg, A., Ledig, C., Guerrero, R., Molina Abril, H., Frangi, A.F. y Rueckert, D. (2016). Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease. PLos ONE, 11 (4)|