%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 22
%N 3
%P e16770
%T Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper
%A Fagherazzi,Guy
%+ Luxembourg Institute of Health, Department of Population Health, Digital Epidemiology Hub, 1 A-B Rue Thomas Edison, Strassen, 1445, Luxembourg, 33 669396334, guy.fagherazzi@lih.lu
%K digital health
%K digital epidemiology
%K deep digital phenotyping
%K digital orthodoxy
%K precision medicine
%K precision health
%K personalized medicine
%K digital phenotyping
%K precision prevention
%K big data
%K omics
%K digitosome
%K data lake
%K digital cohort
%D 2020
%7 3.3.2020
%9 Viewpoint
%J J Med Internet Res
%G English
%X This viewpoint describes the urgent need for more large-scale, deep digital phenotyping to advance toward precision health. It describes why and how to combine real-world digital data with clinical data and omics features to identify someone’s digital twin, and how to finally enter the era of patient-centered care and modify the way we view disease management and prevention.
%M 32130138
%R 10.2196/16770
%U https://www.jmir.org/2020/3/e16770
%U https://doi.org/10.2196/16770
%U http://www.ncbi.nlm.nih.gov/pubmed/32130138