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Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper

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 patien...

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Detalles Bibliográficos
Autor principal: Fagherazzi, Guy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7078624/
https://www.ncbi.nlm.nih.gov/pubmed/32130138
http://dx.doi.org/10.2196/16770
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author Fagherazzi, Guy
author_facet Fagherazzi, Guy
author_sort Fagherazzi, Guy
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description 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.
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spelling pubmed-70786242020-03-25 Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper Fagherazzi, Guy J Med Internet Res Viewpoint 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. JMIR Publications 2020-03-03 /pmc/articles/PMC7078624/ /pubmed/32130138 http://dx.doi.org/10.2196/16770 Text en ©Guy Fagherazzi. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.03.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Viewpoint
Fagherazzi, Guy
Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper
title Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper
title_full Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper
title_fullStr Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper
title_full_unstemmed Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper
title_short Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper
title_sort deep digital phenotyping and digital twins for precision health: time to dig deeper
topic Viewpoint
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7078624/
https://www.ncbi.nlm.nih.gov/pubmed/32130138
http://dx.doi.org/10.2196/16770
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