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, Ziye Xing, Minh Hoang Huynh Nguyen, Audrey Haque, Ankit Mathur, Josh Payne, Ghazal Mazaheri, Jason Kenichi Li, Pramod Kotipalli, Lisa Liao, Rajat Bhasin, Kexin Cha, Benjamin Rolnik, Alessandra Celli, Orit Dagan-Rosenfeld, Emily Higgs, Wenyu Zhou, Camille Lauren Berry, Katherine Grace Winkle, Kévin Contrepois, Utsab Ray, Keith Bettinger, Somalee Datta, Xiao Li () and Michael P. Snyder ()
Additional contact information
Amir Bahmani: Stanford University
Arash Alavi: Stanford University
Thore Buergel: Stanford University
Sushil Upadhyayula: Stanford University
Qiwen Wang: Stanford University
Srinath Krishna Ananthakrishnan: Stanford University
Amir Alavi: Stanford University
Diego Celis: Stanford University
Dan Gillespie: Stanford University
Gregory Young: Stanford University
Ziye Xing: Stanford University
Minh Hoang Huynh Nguyen: Stanford University
Audrey Haque: Stanford University
Ankit Mathur: Stanford University
Josh Payne: Stanford University
Ghazal Mazaheri: Stanford University
Jason Kenichi Li: Stanford University
Pramod Kotipalli: Stanford University
Lisa Liao: Stanford University
Rajat Bhasin: Stanford University
Kexin Cha: Stanford University
Benjamin Rolnik: Stanford University
Alessandra Celli: Stanford University
Orit Dagan-Rosenfeld: Stanford University
Emily Higgs: Stanford University
Wenyu Zhou: Stanford University
Camille Lauren Berry: Stanford University
Katherine Grace Winkle: Stanford University
Kévin Contrepois: Stanford University
Utsab Ray: Stanford University
Keith Bettinger: Stanford University
Somalee Datta: Technology and Digital Solutions, Stanford Medicine
Xiao Li: Stanford University
Michael P. Snyder: Stanford University

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-26040-1

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