This paper is in the following e-collection/theme issue:

New Methods (944) Patient-Reported Outcome Measures (PROMs) (288) Machine Learning (1463) Ecological Momentary Assessment (EMA) (309) mHealth for Data Collection and Research (954) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1397) Hearing Impairment and Hearing Loss (67)

Published on in Vol 22, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15547, first published .
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

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

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

Authors of this article:

Rüdiger Pryss1 Author Orcid Image ;   Winfried Schlee2 Author Orcid Image ;   Burkhard Hoppenstedt3 Author Orcid Image ;   Manfred Reichert3 Author Orcid Image ;   Myra Spiliopoulou4 Author Orcid Image ;   Berthold Langguth2 Author Orcid Image ;   Marius Breitmayer3 Author Orcid Image ;   Thomas Probst5 Author Orcid Image
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