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

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.

Longitudinally Tracking Personal Physiomes for Precision Management of Childhood Epilepsy

“…As such, the first step towards this overarching goal is to enable personalized data acquisition and analytics. Compared with conventional approaches using clinical notes and questionnaires, wearable sensors connected to a cloud computing system represent important technological advancement that enables one to track individuals' personal activity and physiologic parameters proactively and longitudinally to identify clinically concerning events [4,[38][39][40].…”

Towards Accurate Point-of-Care Tests for Tuberculosis in Children

“…The internet of (medical) things—which means medical devices and applications are connected to healthcare systems through online computer networks—provides a continuously growing field in which patients and healthcare workers are connected and machine-to-machine communication allows for the rapid exchange of results [ 107 , 108 , 109 , 110 ]. According to the principles for digital development, open-source platforms—being device- and analytic-agnostic—are key to fostering a sustainable digital development [ 111 , 112 ]. By adding smart algorithms and applications to biosensors, patients are directly engaged and empowered to participate in managing their health and disease.…”

Breaking Barriers in Emerging Biomedical Applications

“…The concept of Ring Signatures that provides privacy properties like Signers Anonymity and Signature Correctness, has been introduced. Some recent research activities address the challenges in collecting, storing, and processing large-scale biomedical datasets across heterogeneous computing and storage platforms [77]. As a result, Personal Health Dashboard (PHD), a secure, scalable, and interoperable platform for the streamlined and cost-effective acquisition, storage, and analysis of large biomedical datasets ranging from wearable biosensor data and multi-omics profiles to clinical data, is presented.…”