TY - JOUR A1 - Allgaier, Johannes A1 - Schlee, Winfried A1 - Langguth, Berthold A1 - Probst, Thomas A1 - Pryss, Rüdiger T1 - Predicting the Gender of Individuals with Tinnitus based on Daily Life Data of the TrackYourTinnitus mHealth Platform JF - Scientific Reports N2 - Tinnitus is an auditory phantom perception in the absence of an external sound stimulation. People with tinnitus often report severe constraints in their daily life. Interestingly, indications exist on gender differences between women and men both in the symptom profile as well as in the response to specific tinnitus treatments. In this paper, data of the TrackYourTinnitus platform (TYT) were analyzed to investigate whether the gender of users can be predicted. In general, the TYT mobile Health crowdsensing platform was developed to demystify the daily and momentary variations of tinnitus symptoms over time. The goal of the presented investigation is a better understanding of gender-related differences in the symptom profiles of users from TYT. Based on two questionnaires of TYT, four machine learning based classifiers were trained and analyzed. With respect to the provided daily answers, the gender of TYT users can be predicted with an accuracy of 81.7%. In this context, worries, difficulties in concentration, and irritability towards the family are the three most important characteristics for predicting the gender. Note that in contrast to existing studies on TYT, daily answers to the worst symptom question were firstly investigated in more detail. It was found that results of this question significantly contribute to the prediction of the gender of TYT users. Overall, our findings indicate gender-related differences in tinnitus and tinnitus-related symptoms. Based on evidence that gender impacts the development of tinnitus, the gathered insights can be considered relevant and justify further investigations in this direction. KW - computer science KW - machine learning KW - psychology KW - signs and symptoms Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-261753 VL - 11 IS - 1 ER - 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 JF - 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281812 SN - 2077-0383 VL - 11 IS - 15 ER - TY - JOUR A1 - Pryss, Rüdiger A1 - Schlee, Winfried A1 - Hoppenstedt, Burkhard A1 - Reichert, Manfred A1 - Spiliopoulou, Myra A1 - Langguth, Berthold A1 - Breitmayer, Marius A1 - Probst, Thomas T1 - Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study JF - Journal of Medical Internet Research N2 - Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient's quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)-Android and iOS-to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. KW - crowdsensing KW - ecological momentary assessment KW - mHealth KW - machine learning KW - mobile operating system differences KW - tinnitus KW - mobile phone Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-229517 VL - 22 IS - 6 ER -