Towards Longitudinal Data Analytics in Parkinson’s Disease

  • Nikos F. Fragopanagos
  • Stefan Kueppers
  • Panagiotis Kassavetis
  • Marco U. Luchini
  • George Roussos
Conference paper
First Online:
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 181)

Abstract

The CloudUPDRS app has been developed as a Class I medical device to assess the severity of motor symptoms for Parkinson’s Disease using a fully automated data capture and signal analysis process based on the standard Unified Parkinson’s Disease Rating Scale. In this paper we report on the design and development of the signal processing 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.

Keywords

Longitudinal Data Analytics People With Parkinson Medical Intelligence Touch Event Emergency Hospital Visit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgments

Project CloudUPDRS: Big Data Analytics for Parkinson’s Disease patient stratification is supported by Innovate UK (Project Number 102160).

References

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Nikos F. Fragopanagos
    • 1
  • Stefan Kueppers
    • 2
    • 3
  • Panagiotis Kassavetis
    • 4
  • Marco U. Luchini
    • 3
  • George Roussos
    • 2
  1. 1.Retechnica Ltd.LondonUK
  2. 2.Birkbeck College, University of LondonLondonUK
  3. 3.Benchmark Performance Ltd.LondonUK
  4. 4.Boston UniversityBostonUSA

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