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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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JMIR Publications
2020
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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 |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-7078624 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT fagherazziguy deepdigitalphenotypinganddigitaltwinsforprecisionhealthtimetodigdeeper |