| Autor: |
Alexander Schmidt-Richberg, Christian Ledig, Ricardo Guerrero, Helena Molina-Abril, Alejandro Frangi, Daniel Rueckert, Alzheimer’s Disease Neuroimaging Initiative |
| Jazyk: |
angličtina |
| Rok vydání: |
2016 |
| Předmět: |
|
| Zdroj: |
PLoS ONE, Vol 11, Iss 4, p e0153040 (2016) |
| Druh dokumentu: |
article |
| ISSN: |
1932-6203 |
| DOI: |
10.1371/journal.pone.0153040 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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