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Smart Learning Using Big and Small Data for Mobile and IOT e-Health

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Abstract

In this chapter, we provide a snapshot of the state-of-the-art research in mobile and IOT e-health studies that leverage AI technologies for making sense of personal health measurement and assessment, as well as for delivering situational, actionable insights in care flows. In recent years, the proliferation of consumer and pervasive health technologies has enabled a whole new generation of sensor-based precision measurement technologies and mobile ecological momentary assessments that are able to capture patient-specific characteristics in context [3–5]. The captured physiomes (i.e., a collection of quantitative and integrated descriptions of the functional behavior of the physiological state of an individual [1]) can help detect physiological macro-phenotypes such as inflammatory response and fatigue [8], as well as critical conditions such as seizure and atrial fibrillation [6, 7]. The accumulated longitudinal records of such phonemes are also expected to capture patterns that can help distinguish individual physiological differences, e.g., being insulin-sensitive or insulin-resistant, which will make a difference in disease diagnosis and prognosis [8].

There are no secrets to success. It is the result of preparation, hard work, and learning from failure.

Colin Powell

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Acknowledgment

Theme 2: The projects Witra-Care and Mobile Care Backup (MoCaB) are funded by German Federal Ministry of Education and Research (grant numbers Witra-Care: 16SV6380; MoCaB: 16SV7472).

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Authors and Affiliations

  1. Viome, Inc., Yorktown Heights, NY, USA

    Pei-Yun Sabrina Hsueh

  2. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA

    Xinyu Hu & Ying Kuen Cheung

  3. Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hanover, Germany

    Dominik Wolff & Michael Marschollek

  4. IBM Watson Research Center, Yorktown Heights, NY, USA

    Jeff Rogers

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  1. Pei-Yun Sabrina Hsueh
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  2. Xinyu Hu
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  3. Ying Kuen Cheung
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  4. Dominik Wolff
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  5. Michael Marschollek
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  6. Jeff Rogers
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Editors and Affiliations

  1. Department of ECE, Duke University, Durham, NC, USA

    Farshad Firouzi

  2. Department of ECE, Duke University, Durham, NC, USA

    Krishnendu Chakrabarty

  3. Radyalis LLC, Austin, TX, USA

    Sani Nassif

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Hsueh, PY.S., Hu, X., Cheung, Y.K., Wolff, D., Marschollek, M., Rogers, J. (2020). Smart Learning Using Big and Small Data for Mobile and IOT e-Health. In: Firouzi, F., Chakrabarty, K., Nassif, S. (eds) Intelligent Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-30367-9_13

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  • DOI: https://doi.org/10.1007/978-3-030-30367-9_13

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