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Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus–stress associations based on the TrackYourTinnitus mobile platform

, , , , , , , and . International Journal of Data Science and Analytics, (2018)

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Are Non-Experts Able to Comprehend Business Process Models--Study Insights Involving Novices and Experts, , , , , , and . arXiv preprint arXiv:2107.02030, (2021)Predicting the presence of tinnitus using ecological momentary assessments, , , , , , , , and . Scientific Reports, 13 (1): 8989 (2023)Patient Empowerment Through Summarization of Discussion Threads on Treatments in a Patient Self-help Forum, , , , , , , , , and 1 other author(s). page 229-233. Springer Singapore, (2018)Best Paper Award (2. Position) at the Int. Conf. on Biomedical and Health Informatics (ICBHI 2017).Expect the gap: A recommender approach to estimate the absenteeism of self-monitoring mHealth app users., , , , and . DSAA, page 1-10. IEEE, (2022)Deep Learning End-to-End Approach for the Prediction of Tinnitus based on EEG Data*., , , , and . EMBC, page 816-819. IEEE, (2021)Free Technical Solutions for Ecological Momentary Assessments - Searching GitHub plus Google., , , , , , , and . CSCI, page 1778-1781. IEEE, (2022)Circadian Conditional Granger Causalities on Ecological Momentary Assessment Data from an mHealth App., , , , , and . CBMS, page 354-359. IEEE, (2021)Entity-level stream classification: exploiting entity similarity to label the future observations referring to an entity., , , , , , , and . Int. J. Data Sci. Anal., 9 (1): 1-15 (2020)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)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)