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    Towards longitudinal data analytics in Parkinson's Disease

    Fragopanagos, N. and Kueppers, S. and Kassavetis, P. and Luchini, M.U. and Roussos, George (2017) Towards longitudinal data analytics in Parkinson's Disease. In: Giokas, K. and Bokor, L. and Hopfg, F. (eds.) eHealth 360°: International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 181. Springer, pp. 56-61. ISBN 9783319496542.

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    Abstract

    The CloudUPDRS app has been developed as a Class I med- ical device to assess the severity of motor symptoms for Parkinson’s Disease using a fully automated data capture and signal analysis pro- cess based on the standard Unified Parkinson’s Disease Rating Scale. In this paper we report on the design and development of the signal pro- cessing and longitudinal data analytics microservices developed to carry out these assessments and to forecast the long-term development of the disease. We also report on early findings from the application of these techniques in the wild with a cohort of early adopters.

    Metadata

    Item Type: Book Section
    Additional Information: Series ISSN: 1867-8211. The final publication is available at Springer via the link above.
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: George Roussos
    Date Deposited: 16 Jan 2017 11:31
    Last Modified: 28 Jul 2019 22:52
    URI: http://eprints.bbk.ac.uk/id/eprint/16629

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