Artículo
Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease
Autor/es | Schmidt-Richberg, Alexander
Ledig, Christian Guerrero, Ricardo Molina Abril, Helena Frangi, Alejandro F. Rueckert, Daniel |
Departamento | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII) |
Fecha de publicación | 2016 |
Fecha de depósito | 2021-06-18 |
Publicado en |
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Resumen | 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. |
Agencias financiadoras | European Commission (EC) |
Identificador del proyecto | FP7-ICT-2011-9-601055 |
Cita | 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) |
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