Ecole d'ingénieur et centre de recherche en Sciences du numérique

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

Lorenzi, Marco; Filippone, Maurizio; Frisoni, Giovanni; Alexander, Daniel; Ourselin, Sébastien

NeuroImage, Elsevier, October 24, 2017

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.

Doi Hal Bibtex

Titre:Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer's disease
Mots Clés:Alzheimer's disease; Diagnosis; Disease progression modeling; Gaussian process; Clinical trials
Type:Journal
Langue:English
Ville:
Date:
Département:Data Science
Eurecom ref:5352
Copyright: © 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
Bibtex: @article{EURECOM+5352, doi = {http://doi.org/10.1016/j.neuroimage.2017.08.059}, year = {2017}, month = {10}, title = {{P}robabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in {A}lzheimer's disease}, author = {{L}orenzi, {M}arco and {F}ilippone, {M}aurizio and {F}risoni, {G}iovanni and {A}lexander, {D}aniel and {O}urselin, {S}{\'e}bastien}, journal = {{N}euro{I}mage, {E}lsevier, {O}ctober 24, 2017}, url = {http://www.eurecom.fr/publication/5352} }
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