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Publication Detail
An event-based disease progression model and its application to familial Alzheimer's disease
  • Publication Type:
  • Authors:
    Fonteijn HM, Clarkson MJ, Modat M, Barnes J, Lehmann M, Ourselin S, Fox NC, Alexander DC
  • Publication date:
  • Pagination:
    748, 759
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    6801 LNCS
  • ISBN-13:
  • Status:
  • Print ISSN:
This study introduces a novel event-based model for disease progression. The model describes disease progression as a series of events. An event can consist of a significant change in symptoms or in tissue. We construct a forward model that relates heterogeneous measurements from a whole cohort of patients and controls to the event sequence and fit the model with a Bayesian estimation framework. The model does not rely on a priori classification of patients and therefore has the potential to describe disease progression in much greater detail than previous approaches. We demonstrate our model on serial T1 MRI data from a familial Alzheimer's disease cohort. We show progression of neuronal atrophy on a much finer level than previous studies, while confirming progression patterns from pathological studies, and integrate clinical events into the model. © 2011 Springer-Verlag.
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