dc.creator | Schmidt-Richberg, Alexander | es |
dc.creator | Ledig, Christian | es |
dc.creator | Guerrero, Ricardo | es |
dc.creator | Molina Abril, Helena | es |
dc.creator | Frangi, Alejandro F. | es |
dc.creator | Rueckert, Daniel | es |
dc.date.accessioned | 2021-06-18T09:56:33Z | |
dc.date.available | 2021-06-18T09:56:33Z | |
dc.date.issued | 2016 | |
dc.identifier.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) | |
dc.identifier.issn | 1932-6203 | es |
dc.identifier.uri | https://hdl.handle.net/11441/111882 | |
dc.description.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, 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. | es |
dc.description.sponsorship | European Commission FP7-ICT-2011-9-601055 | es |
dc.format | application/pdf | es |
dc.format.extent | 27 | es |
dc.language.iso | eng | es |
dc.publisher | Public Library of Science | es |
dc.relation.ispartof | PLos ONE, 11 (4) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Matemática Aplicada I (ETSII) | es |
dc.relation.projectID | FP7-ICT-2011-9-601055 | es |
dc.relation.publisherversion | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0153040 | es |
dc.identifier.doi | 10.1371/journal.pone.0153040 | es |
dc.journaltitle | PLos ONE | es |
dc.publication.volumen | 11 | es |
dc.publication.issue | 4 | es |
dc.contributor.funder | European Commission (EC) | es |