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Understanding adherence to the recording of ecological momentary assessments in the example of tinnitus monitoring

, , , , , , , and . Scientific Reports, (December 2020)
DOI: 10.1038/s41598-020-79527-0

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Expect the gap: A recommender approach to estimate the absenteeism of self-monitoring mHealth app users., , , , and . DSAA, page 1-10. IEEE, (2022)Juxtaposing Medical Centers Using Different Questionnaires Through Score Predictors, , , , , , , , , and 1 other author(s). Frontiers in Neuroscience, (March 2022)Using Big Data to Develop a Clinical Decision Support System for Tinnitus Treatment, , , , , , , , , and 9 other author(s). page 175-189. Springer International Publishing, Cham, (2021)Parsimonious predictors for medical decision support: Minimizing the set of questionnaires used for tinnitus outcome prediction, , , , , and . Expert Systems with Applications, (April 2024)Predicting User Engagement in mHealth Apps with Neighborhood-based Approaches, , , , and . 2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS), page 391-397. IEEE, (June 2024)Training and Validating a Treatment Recommender with Partial Verification Evidence., , , , , , , , , and 4 other author(s). CoRR, (2024)Discovery of Patient Phenotypes through Multi-layer Network Analysis on the Example of Tinnitus., , , , , and . DSAA, page 1-10. IEEE, (2021)A Similarity-Guided Framework for Error-Driven Discovery of Patient Neighbourhoods in EMA Data., , , , , , and . IDA, volume 13876 of Lecture Notes in Computer Science, page 459-471. Springer, (2023)ICE: Interactive Classification Rule Exploration on Epidemiological Data., , , , and . CBMS, page 606-611. IEEE Computer Society, (2017)Understanding adherence to the recording of ecological momentary assessments in the example of tinnitus monitoring, , , , , , , and . Scientific Reports, (December 2020)