TY  - JOUR
A1  - Allgaier, Johannes
A1  - Schlee, Winfried
A1  - Probst, Thomas
A1  - Pryss, Rüdiger
T1  - Prediction of tinnitus perception based on daily life mHealth data using country origin and season
T2  - Journal of Clinical Medicine
N2  - Tinnitus is an auditory phantom perception without external sound stimuli. This chronic perception can severely affect quality of life. Because tinnitus symptoms are highly heterogeneous, multimodal data analyses are increasingly used to gain new insights. MHealth data sources, with their particular focus on country- and season-specific differences, can provide a promising avenue for new insights. Therefore, we examined data from the TrackYourTinnitus (TYT) mHealth platform to create symptom profiles of TYT users. We used gradient boosting engines to classify momentary tinnitus and regress tinnitus loudness, using country of origin and season as features. At the daily assessment level, tinnitus loudness can be regressed with a mean absolute error rate of 7.9% points. In turn, momentary tinnitus can be classified with an F1 score of 93.79%. Both results indicate differences in the tinnitus of TYT users with respect to season and country of origin. The significance of the features was evaluated using statistical and explainable machine learning methods. It was further shown that tinnitus varies with temperature in certain countries. The results presented show that season and country of origin appear to be valuable features when combined with longitudinal mHealth data at the level of daily assessment.
KW  - tinnitus
KW  - gradient boosting machine
KW  - mobile health
KW  - machine learning
KW  - multimodal data
KW  - explainable machine learning
Y1  - 2022
UR  - https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/28181
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-281812
SN  - 2077-0383
VL  - 11
IS  - 15
ER  -