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Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer's disease

Abstract : Disease progression modeling (DPM) of Alzheimer's disease (AD) aims at revealing long term pathological trajectories from short term clinical data. Along with the ability of providing a data-driven description of the natural evolution of the pathology, DPM has the potential of representing a valuable clinical instrument for automatic diagnosis, by explicitly describing the biomarker transition from normal to pathological stages along the disease time axis. In this work we reformulated DPM within a probabilistic setting to quantify the diagnostic uncertainty of individual disease severity in an hypothetical clinical scenario, with respect to missing measurements, biomarkers, and follow-up information.
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https://hal.archives-ouvertes.fr/hal-01617750
Contributor : Marco Lorenzi <>
Submitted on : Tuesday, October 17, 2017 - 9:14:47 AM
Last modification on : Friday, October 25, 2019 - 5:12:05 PM
Document(s) archivé(s) le : Thursday, January 18, 2018 - 12:39:48 PM

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Marco Lorenzi, Maurizio Filippone, Giovanni Frisoni, Daniel Alexander, Sébastien Ourselin. Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer's disease. NeuroImage, Elsevier, 2017. ⟨hal-01617750⟩

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