Your browser doesn't support javascript.
A scalable, secure, and interoperable platform for deep data-driven health management.
Bahmani, Amir; Alavi, Arash; Buergel, Thore; Upadhyayula, Sushil; Wang, Qiwen; Ananthakrishnan, Srinath Krishna; Alavi, Amir; Celis, Diego; Gillespie, Dan; Young, Gregory; Xing, Ziye; Nguyen, Minh Hoang Huynh; Haque, Audrey; Mathur, Ankit; Payne, Josh; Mazaheri, Ghazal; Li, Jason Kenichi; Kotipalli, Pramod; Liao, Lisa; Bhasin, Rajat; Cha, Kexin; Rolnik, Benjamin; Celli, Alessandra; Dagan-Rosenfeld, Orit; Higgs, Emily; Zhou, Wenyu; Berry, Camille Lauren; Van Winkle, Katherine Grace; Contrepois, Kévin; Ray, Utsab; Bettinger, Keith; Datta, Somalee; Li, Xiao; Snyder, Michael P.
  • Bahmani A; Department of Genetics, Stanford University, Stanford, CA, USA.
  • Alavi A; Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA.
  • Buergel T; Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
  • Upadhyayula S; Department of Genetics, Stanford University, Stanford, CA, USA.
  • Wang Q; Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA.
  • Ananthakrishnan SK; Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
  • Alavi A; Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
  • Celis D; Department of Genetics, Stanford University, Stanford, CA, USA.
  • Gillespie D; Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
  • Young G; Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Xing Z; Department of Genetics, Stanford University, Stanford, CA, USA.
  • Nguyen MHH; Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
  • Haque A; Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Mathur A; Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
  • Payne J; Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
  • Mazaheri G; Department of Genetics, Stanford University, Stanford, CA, USA.
  • Li JK; Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
  • Kotipalli P; Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Liao L; Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
  • Bhasin R; Department of Genetics, Stanford University, Stanford, CA, USA.
  • Cha K; Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
  • Rolnik B; Department of Genetics, Stanford University, Stanford, CA, USA.
  • Celli A; Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA.
  • Dagan-Rosenfeld O; Department of Genetics, Stanford University, Stanford, CA, USA.
  • Higgs E; Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA.
  • Zhou W; Department of Genetics, Stanford University, Stanford, CA, USA.
  • Berry CL; Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, CA, USA.
  • Van Winkle KG; Department of Genetics, Stanford University, Stanford, CA, USA.
  • Contrepois K; Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
  • Ray U; Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Bettinger K; Department of Genetics, Stanford University, Stanford, CA, USA.
  • Datta S; Stanford Healthcare Innovation Lab, Stanford University, Stanford, CA, USA.
  • Li X; Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Snyder MP; Department of Genetics, Stanford University, Stanford, CA, USA.
Nat Commun ; 12(1): 5757, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1447304
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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Medical Records Systems, Computerized / Data Science Type of study: Cohort study / Observational study / Prognostic study Limits: Humans Language: English Journal: Nat Commun Journal subject: Biology / Science Year: 2021 Document Type: Article Affiliation country: S41467-021-26040-1

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Medical Records Systems, Computerized / Data Science Type of study: Cohort study / Observational study / Prognostic study Limits: Humans Language: English Journal: Nat Commun Journal subject: Biology / Science Year: 2021 Document Type: Article Affiliation country: S41467-021-26040-1