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

Amir Bahmani,Amir Alavi,Jason Kenichi Li,Kévin Contrepois,Emily Higgs,Orit Dagan‐Rosenfeld ,Srinath Krishna Ananthakrishnan,Ghazal Mazaheri,Minh Nguyen ,Dan Gillespie,Rajat Bhasin,Sushil Upadhyayula,Camille Lauren Berry,Keith Bettinger,Benjamin Rolnik,Somalee Datta,Alessandra Celli,Lisa Liao,Arash Alavi,Xiao Li,Audrey Haque,Qiwen Wang,Thore Buergel,Ziye Xing,Kexin Cha,Gregory S Young ,Ankit Mathur,Katherine Grace Van Winkle,Wenyu Zhou,Josh Payne,Diego Celis,Pramod Kotipalli,Utsab Ray,M Snyder

doi:10.1038/s41467-021-26040-1

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.

Highlights

The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling is rapidly transforming our healthcare systems
We show how Personal Health Dashboard (PHD) can be used for collection and visualization of diverse datasets at a personal level, as an infrastructure for detection of presymptomatic COVID-19 cases, and biological characterization of insulin-resistance heterogeneity
Through secure integration of omics data centers, the PHD platform allows for joint analysis of wearables data with multi-omics and clinical data on its machine learning (ML) cluster

Summary

Introduction

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. Biomedical data continue to grow in scale, diversity, and complexity even as the costs of data access, storage, distribution, and analysis undergo rapid change[1,2,3,4,5] These changes present researchers and clinicians with three major challenges: (1) Scalability: The majority of healthcare applications need to handle a large number of participants and different data types and tasks while preserving rapid, actionable turnaround times (e.g., cardiovascular monitoring or infectious disease detection). Researchers must be able to readily collect, store, and process large-scale biomedical datasets across multiple computing and storage platforms that are interoperable and low cost To address these three challenges, we developed the Personal Health Dashboard (PHD) platform, a secure, scalable, and interoperable platform that enables the streamlined and costeffective acquisition, storage, and analysis of large biomedical datasets ranging from wearable biosensor data and multi-omics profiles to clinical data. We expect it to be of wide utility

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