Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study.

Journal: Journal of medical Internet research
PMID: 32602842

Abstract

BACKGROUND: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient's quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)-Android and iOS-to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider.

Authors

  • Rüdiger Pryss
    Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany.
  • Winfried Schlee
    Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany.
  • Burkhard Hoppenstedt
    Institute of Databases and Information Systems, Ulm University, Ulm, Germany.
  • Manfred Reichert
    Institute of Databases and Information Systems, Ulm University, Ulm, Germany.
  • Myra Spiliopoulou
    Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, 39106, Germany.
  • Berthold Langguth
    Department of Psychiatry and Psychotherapy, University of Regensburg.
  • Marius Breitmayer
    Institute of Databases and Information Systems, Ulm University, Ulm, Germany.
  • Thomas Probst
    Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems, Austria.

Keywords

External Resources

Popular Topics
Recent Journals