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An event-based disease progression model and its application to familial Alzheimer's disease.
Inf Process Med Imaging.
(pp. 748 - 759).
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.
|Title:||An event-based disease progression model and its application to familial Alzheimer's disease.|
|Keywords:||Algorithms, Alzheimer Disease, Brain, Computer Simulation, Disease Progression, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Models, Biological, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > Neurodegenerative Diseases
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
UCL > School of BEAMS > Faculty of Engineering Science > Medical Physics and Bioengineering
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