Evidence map›Paper›PMID 34599181›Full record

ArticleNature communications2021

A scalable, secure, and interoperable platform for deep data-driven health management.

Amir Bahmani, Arash Alavi, Thore Buergel, Sushil Upadhyayula, Qiwen Wang, Srinath Krishna Ananthakrishnan, Amir Alavi, Diego Celis, Dan Gillespie, Gregory Young and 24 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
4.6field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 54 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Clinical Research Informatics: a Decade-in-Review.Yearbook of medical informatics · 2024
    Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Metaverse Wearables for Immersive Digital Healthcare: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Review
  12. Review
  13. Multi-Omics Profiling for Health.Molecular & cellular proteomics : MCP · 2023
    Review
  14. Review
  15. Article
  16. Review
  17. Breaking Barriers in Emerging Biomedical Applications.Entropy (Basel, Switzerland) · 2022
    Review
  18. Article
  19. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

34 authors at 4 institutions in 1 country.

Amir Bahmani *Department of Genetics, Stanford University, Stanford, CA, USA.
Arash Alavi *Department of Genetics, Stanford University, Stanford, CA, USA.ORCID 0000-0002-7245-6889
Thore Buergel *Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.ORCID 0000-0003-1159-007X
Sushil UpadhyayulaDepartment of Genetics, Stanford University, Stanford, CA, USA.
Qiwen WangDepartment of Genetics, Stanford University, Stanford, CA, USA.
Srinath Krishna AnanthakrishnanStanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
Amir AlaviStanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
Diego CelisDepartment of Genetics, Stanford University, Stanford, CA, USA.
Dan GillespieStanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.ORCID 0000-0003-0066-5136
Gregory YoungDepartment of Genetics, Stanford University, Stanford, CA, USA.
Ziye XingDepartment of Genetics, Stanford University, Stanford, CA, USA.
Minh Hoang Huynh NguyenDepartment of Genetics, Stanford University, Stanford, CA, USA.
Audrey HaqueDepartment of Genetics, Stanford University, Stanford, CA, USA.
Ankit MathurDepartment of Genetics, Stanford University, Stanford, CA, USA.
Josh PayneDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID 0000-0002-1312-0138
Ghazal MazaheriDepartment of Genetics, Stanford University, Stanford, CA, USA.
Jason Kenichi LiDepartment of Genetics, Stanford University, Stanford, CA, USA.
Pramod KotipalliDepartment of Genetics, Stanford University, Stanford, CA, USA.
Lisa LiaoDepartment of Genetics, Stanford University, Stanford, CA, USA.
Rajat BhasinStanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
Kexin ChaDepartment of Genetics, Stanford University, Stanford, CA, USA.
Benjamin RolnikDepartment of Genetics, Stanford University, Stanford, CA, USA.
Alessandra CelliDepartment of Genetics, Stanford University, Stanford, CA, USA.
Orit Dagan-RosenfeldDepartment of Genetics, Stanford University, Stanford, CA, USA.
Emily HiggsDepartment of Genetics, Stanford University, Stanford, CA, USA.
Wenyu ZhouDepartment of Genetics, Stanford University, Stanford, CA, USA.
Camille Lauren BerryDepartment of Genetics, Stanford University, Stanford, CA, USA.
Katherine Grace Van WinkleDepartment of Genetics, Stanford University, Stanford, CA, USA.
Kévin ContrepoisDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID 0000-0001-9678-5161
Utsab RayDepartment of Genetics, Stanford University, Stanford, CA, USA.
Keith BettingerDepartment of Genetics, Stanford University, Stanford, CA, USA.
Somalee DattaTechnology and Digital Solutions, Stanford Medicine, Stanford, CA, USA.
Xiao LiDepartment of Genetics, Stanford University, Stanford, CA, USA. xiao.li9@case.edu.
Michael P SnyderDepartment of Genetics, Stanford University, Stanford, CA, USA. mpsnyder@stanford.edu.ORCID 0000-0003-0784-7987
Stanford Health Care · USStanford University · USCase Western Reserve University · USStanford Medicine · US

Funding

Multi-Modal Wireless COVID Monitoring & Infection Alerts for Concentrated PopulationsR01NR020105 · NINR · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P. · 2021 to 2023
$3.3M
Computational appliance: a supercomputer for modern biomedical researchS10OD023452 · OD · STANFORD UNIVERSITY · PI DATTA, SOMALEE · 2017 to 2017
$600k
NIH HHS S10 OD023452NINR NIH HHS R01 NR020105
6 · The paper itself

Abstract

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.

Indexed as

Medical Records Systems, ComputerizedBig DataComputer SecurityData AnalysisData ScienceHealth Information InteroperabilityHumansInformation Storage and RetrievalSoftware

Identifiers

PMID34599181
PMCPMC8486823
OpenAlexW3202166954

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.