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An Architecture to Support Real-World Studies that Investigate the Autonomic Nervous System

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Heterogeneous Data Management, Polystores, and Analytics for Healthcare (DMAH 2019, Poly 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11721))

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Abstract

Diabetes is a chronic disease with complications related to the autonomic nervous system (ANS) that can affect quality of life and lead to mortality. Clinicians and researchers currently rely on subjective and/or invasive means that don’t necessarily translate to real-world setting when assessing severity of certain diabetes complications. We elicited use-cases of studies aimed at understanding ANS in the context of diabetes to gather system requirements for designing an architecture to support sensor-based studies. Real-world studies would need to be capable of gathering contextual data as well as proxies for ANS symptoms from digital markers from an evolving sensor landscape, while also supporting the data needs of researchers before, during, and after data acquisition. The proposed architecture makes use of open source and commercially available mobile health technologies, and informatics platforms to meet the design criteria. Building and testing a prototype of the proposed architecture is planned to confirm the system performs as expected.

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Acknowledgements

This research is supported by NIH/NIDDK Ruth L. Kirschstein National Research Service Award, Diabetes & Metabolism Research Center at the University of Utah, the England Family Foundation, the Ardene Bullard “Of Love” Tennis Tournament, Jacobsen Construction, NIH/NCATS UL1TR002538, and NIH/NIBIB U54EB021973. Computational resources were provided by the Utah Center for High Performance Computing, which is partially funded by the NIH Shared Instrumentation.

Computational resources were provided by the Utah Center for High Performance Computing, which is partially funded by the NIH Shared Instrumentation Grant 1S10OD021644-01A1.

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Authors and Affiliations

  1. Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, 84108, USA

    Danielle Groat, Ramkiran Gouripeddi & Julio C. Facelli

  2. Center for Clinical and Translational Science, University of Utah, Salt Lake City, UT, 84108, USA

    Ramkiran Gouripeddi, Randy Madsen & Julio C. Facelli

  3. Department of Endocrinology, University of Utah, Salt Lake City, UT, 84108, USA

    Yu Kuei Lin

Authors
  1. Danielle Groat
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  2. Ramkiran Gouripeddi
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  3. Randy Madsen
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  4. Yu Kuei Lin
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  5. Julio C. Facelli
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Corresponding author

Correspondence to Danielle Groat .

Editor information

Editors and Affiliations

  1. Massachusetts Institute of Technology, Lexington, MA, USA

    Vijay Gadepally

  2. Intel Corporation, Portland, OR, USA

    Timothy Mattson

  3. Massachusetts Institute of Technology, Cambridge, MA, USA

    Michael Stonebraker

  4. Stony Brook University, Stony Brook, NY, USA

    Fusheng Wang

  5. University of Washington, Seattle, WA, USA

    Gang Luo

  6. Google, Mountain View, CA, USA

    Yanhui Laing

  7. Lucerne University of Applied Sciences, Rotkreuz, Switzerland

    Alevtina Dubovitskaya

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Groat, D., Gouripeddi, R., Madsen, R., Lin, Y.K., Facelli, J.C. (2019). An Architecture to Support Real-World Studies that Investigate the Autonomic Nervous System. In: Gadepally, V., et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2019 2019. Lecture Notes in Computer Science(), vol 11721. Springer, Cham. https://doi.org/10.1007/978-3-030-33752-0_14

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  • DOI: https://doi.org/10.1007/978-3-030-33752-0_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33751-3

  • Online ISBN: 978-3-030-33752-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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