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Predicting the gender of individuals with tinnitus based on daily life data of the TrackYourTinnitus mHealth platform

, , , , and . Scientific Reports, 11 (1): 18375 (2021)
DOI: 10.1038/s41598-021-96731-8

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Outpatient Tinnitus Clinic, Self-Help Web Platform, or Mobile Application to Recruit Tinnitus Study Samples?, , , , , , , , , and 1 other author(s). Frontiers in Aging Neuroscience, (2017)Studying the Potential of Multi-Target Classification on Patient Screening Data to Predict Dropout Cases, , , , , , , and . Proc. of the 31th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS18), page 239-243. (2018)Review of Smart Services for Tinnitus Self-Help, Diagnostics and Treatments, , , , , , and . Frontiers in neuroscience, (2018)When Can I Expect the mHealth User to Return? Prediction Meets Time Series with Gaps., , , and . AIME, volume 13263 of Lecture Notes in Computer Science, page 310-320. Springer, (2022)Prediction of declining engagement to self-monitoring apps on the example of tinnitus mHealth data., , , , , , , , and . CBMS, page 228-233. IEEE, (2022)Comprehensive insights into the TrackYourTinnitus database., , , , , , , , and . FNC/MobiSPC, volume 175 of Procedia Computer Science, page 28-35. Elsevier, (2020)Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios, , , , , , , , , and 1 other author(s). JoVE (Journal of Visualized Experiments), (2020)Medical Device Regulation: Efforts for mHealth Apps during the COVID-19 Pandemic—An Experience Report of Corona Check and Corona Health, , , , , , , and . J, 4 (2): 206-222 (2021)Reasons for Discontinuing Active Participation on the Internet Forum Tinnitus Talk: Mixed Methods Citizen Science Study, , , , , , , , , and 3 other author(s). JMIR Form Res, 5 (4): e21444 (2021)Towards a unification of treatments and interventions for tinnitus patients: The EU research and innovation action UNITI, , , , , , , , , and 70 other author(s). 260, page 441-451. Elsevier, (2021)