A scalable, secure, and interoperable platform for deep data-driven health management.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
01 10 2021
01 10 2021
Historique:
received:
24
08
2020
accepted:
23
08
2021
entrez:
2
10
2021
pubmed:
3
10
2021
medline:
21
10
2021
Statut:
epublish
Résumé
The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.
Identifiants
pubmed: 34599181
doi: 10.1038/s41467-021-26040-1
pii: 10.1038/s41467-021-26040-1
pmc: PMC8486823
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5757Subventions
Organisme : NIH HHS
ID : S10 OD023452
Pays : United States
Informations de copyright
© 2021. The Author(s).
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