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.
Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer's disease
NeuroImage, Elsevier, October 24, 2017
© Elsevier. Personal use of this material is permitted. The definitive version of this paper was published in NeuroImage, Elsevier, October 24, 2017 and is available at : http://doi.org/10.1016/j.neuroimage.2017.08.059
PERMALINK : https://www.eurecom.fr/publication/5352