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Prediction of tinnitus perception based on daily life mHealth data using country origin and season

Please always quote using this URN: urn:nbn:de:bvb:20-opus-281812
  • 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 boostingTinnitus 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.show moreshow less

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Metadaten
Author: Johannes Allgaier, Winfried Schlee, Thomas Probst, Rüdiger Pryss
URN:urn:nbn:de:bvb:20-opus-281812
Document Type:Journal article
Faculties:Medizinische Fakultät / Institut für Klinische Epidemiologie und Biometrie
Language:English
Parent Title (English):Journal of Clinical Medicine
ISSN:2077-0383
Year of Completion:2022
Volume:11
Issue:15
Article Number:4270
DOI:https://doi.org/10.3390/jcm11154270
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Tag:explainable machine learning; gradient boosting machine; machine learning; mobile health; multimodal data; tinnitus
Release Date:2023/02/08
Date of first Publication:2022/07/22
EU-Project number / Contract (GA) number:722046
EU-Project number / Contract (GA) number:848261
OpenAIRE:OpenAIRE
Open-Access-Publikationsfonds / Förderzeitraum 2022
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International