A method for statistical learning in large databases of heterogeneous imaging, cognitive and behavioral data.

Abstract : The aim of this study is to develop a generative and probabilistic statistical learning model for the joint analysis of heterogeneous biomedical data. The model will be applied to the investigation of neurological disorders from collections of brain imaging, body sensors, biological and clinical data available in current large-scale health databases. The resulting methodological framework will be tested on the UK Biobank, as well as on pathology-specific clinical data, as provided by the ADNI, or INSIGHT initiatives.
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Poster communications
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https://hal.inria.fr/hal-01827389
Contributor : Luigi Antelmi <>
Submitted on : Monday, July 2, 2018 - 4:11:18 PM
Last modification on : Thursday, February 7, 2019 - 5:09:48 PM
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Luigi Antelmi, Marco Lorenzi, Valeria Manera, Philippe Robert, Nicholas Ayache. A method for statistical learning in large databases of heterogeneous imaging, cognitive and behavioral data.. EPICLIN 2018 - 12ème Conférence Francophone d’Epidémiologie Clinique / CLCC 2018 - 25èmes Journées des statisticiens des Centre de Lutte Contre le Cancer, May 2018, Nice, France. Elsevier, Revue d'Épidémiologie et de Santé Publique, 66 (3), pp.S180, 2018, 12e Conférence francophone d’Épidémiologie clinique 25e Journée des statisticiens des Centres de lutte contre le cancer. ⟨10.1016/j.respe.2018.03.306⟩. ⟨hal-01827389⟩

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