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
Article Authors Cited by (15) Tweetations (5) Metrics

Journals

  1. Winter M, Pryss R, Probst T, Reichert M. Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study. Sensors 2020;20(16):4561 View
  2. Cunha B, Rodrigues K, Zaine I, da Silva E, Viel C, Pimentel M. Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports. Journal of Medical Internet Research 2021;23(7):e24278 View
  3. Beierle F, Schobel J, Vogel C, Allgaier J, Mulansky L, Haug F, Haug J, Schlee W, Holfelder M, Stach M, Schickler M, Baumeister H, Cohrdes C, Deckert J, Deserno L, Edler J, Eichner F, Greger H, Hein G, Heuschmann P, John D, Kestler H, Krefting D, Langguth B, Meybohm P, Probst T, Reichert M, Romanos M, Störk S, Terhorst Y, Weiß M, Pryss R. Corona Health—A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic. International Journal of Environmental Research and Public Health 2021;18(14):7395 View
  4. Allgaier J, Schlee W, Langguth B, Probst T, Pryss R. Predicting the gender of individuals with tinnitus based on daily life data of the TrackYourTinnitus mHealth platform. Scientific Reports 2021;11(1) View
  5. Wang Z, Xiong H, Zhang J, Yang S, Boukhechba M, Zhang D, Barnes L, Dou D. From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques. IEEE Internet of Things Journal 2022;9(17):15413 View
  6. Sun Y, Ding W, Shu L, Li K, Zhang Y, Zhou Z, Han G. On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture: A Vision. IEEE Systems Journal 2022;16(1):132 View
  7. Galetsi P, Katsaliaki K, Kumar S. Exploring benefits and ethical challenges in the rise of mHealth (mobile healthcare) technology for the common good: An analysis of mobile applications for health specialists. Technovation 2023;121:102598 View
  8. Breitmayer M, Stach M, Kraft R, Allgaier J, Reichert M, Schlee W, Probst T, Langguth B, Pryss R. Predicting the presence of tinnitus using ecological momentary assessments. Scientific Reports 2023;13(1) View
  9. S Annamalai A, Vijayakumar R, Vellaisamy P, Nagarajan M. Impact of Health Information Technology Tools on Patient Safety in the Indian Healthcare Industry. The Open Biomedical Engineering Journal 2023;17(1) View
  10. Kumar D, Ingole A, Choudhari S. Towards Ideal Health Ecosystem With Artificial Intelligence-Driven Medical Services in India: An Overview. Cureus 2023 View
  11. Setitra M, Fan M. Detection of DDoS attacks in SDN-based VANET using optimized TabNet. Computer Standards & Interfaces 2024;90:103845 View
  12. Sarnicka I, Raj-Koziak D, Skarzynski H, Fludra M, Karendys- Łuszcz K, Gos E. TRENDS IN THE ADVANCEMENT OF MOBILE APPLICATIONS FOR THE DIAGNOSIS AND TREATMENT OF TINNITUS: A COMPREHENSIVE REVIEW OF SCIENTIFIC LITERATURE. Journal of Hearing Science 2024;14(2):9 View

Books/Policy Documents

  1. Simões P, de Oliveira F, da Silva C, dos Santos P, Tanaka H. Current Trends in Biomedical Engineering. View