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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Fonteijn, H. M., Clarkson, M. J., Modat, M., Barnes, J., Lehmann, M., Ourselin, S., … Alexander, D. C. (2011). An event-based disease progression model and its application to familial Alzheimer’s disease. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6801 LNCS, pp. 748–759). https://doi.org/10.1007/978-3-642-22092-0_61
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