Predicting the gender of individuals with tinnitus based on daily life data of the TrackYourTinnitus mHealth platform

Allgaier, Johannes; Schlee, Winfried; Langguth, Berthold; Probst, Thomas; Pryss, Rüdiger

doi:10.1038/s41598-021-96731-8

Cited by 7 publications

(5 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The features are perception , loudness , and stressfulness of tinnitus, as well as the current mood , arousal and stress level of a user, the concentration level while filling out the questionnaire, and perception of the worst tinnitus symptom . A detailed description of the features was already done in previous works 21 . Of note, the time delta of two assessments of one user at t n e x t and t n o w varies between users.…”

Section: Methodsmentioning

confidence: 99%

Practical approaches in evaluating validation and biases of machine learning applied to mobile health studies

Allgaier,

Pryss

2024

Commun Med

Self Cite

View full text Add to dashboard Cite

Background Machine learning (ML) models are evaluated in a test set to estimate model performance after deployment. The design of the test set is therefore of importance because if the data distribution after deployment differs too much, the model performance decreases. At the same time, the data often contains undetected groups. For example, multiple assessments from one user may constitute a group, which is usually the case in mHealth scenarios. Methods In this work, we evaluate a model’s performance using several cross-validation train-test-split approaches, in some cases deliberately ignoring the groups. By sorting the groups (in our case: Users) by time, we additionally simulate a concept drift scenario for better external validity. For this evaluation, we use 7 longitudinal mHealth datasets, all containing Ecological Momentary Assessments (EMA). Further, we compared the model performance with baseline heuristics, questioning the essential utility of a complex ML model. Results Hidden groups in the dataset leads to overestimation of ML performance after deployment. For prediction, a user’s last completed questionnaire is a reasonable heuristic for the next response, and potentially outperforms a complex ML model. Because we included 7 studies, low variance appears to be a more fundamental phenomenon of mHealth datasets. Conclusions The way mHealth-based data are generated by EMA leads to questions of user and assessment level and appropriate validation of ML models. Our analysis shows that further research needs to follow to obtain robust ML models. In addition, simple heuristics can be considered as an alternative for ML. Domain experts should be consulted to find potentially hidden groups in the data.

show abstract

Section: Methodsmentioning

confidence: 99%

Practical approaches in evaluating validation and biases of machine learning applied to mobile health studies

Allgaier,

Pryss

2024

Commun Med

Self Cite

View full text Add to dashboard Cite

show abstract

“…How unpleasant is your tinnitus at present? Emotional value Ts6 Average of [ and ] Cognitive-Emotional Value Ts36 Allgaier et al 49 used similar questions for daily monitoring tinnitus in the “TrackYourTinnitus” project. …”

Section: Tinnitus Causality Modelmentioning

confidence: 99%

“… Allgaier et al 49 used similar questions for daily monitoring tinnitus in the “TrackYourTinnitus” project. …”

Section: Tinnitus Causality Modelmentioning

confidence: 99%

Conceptual framework for tinnitus: a cognitive model in practice

Ghodratitoostani,

Vaziri,

Miranda Neto

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Tinnitus is a conscious attended awareness perception of sourceless sound. Widespread theoretical and evidence-based neurofunctional and psychological models have tried to explain tinnitus-related distress considering the influence of psychological and cognitive factors. However, tinnitus models seem to be less focused on causality, thereby easily misleading interpretations. Also, they may be incapable of individualization. This study proposes a Conceptual Cognitive Framework (CCF) providing insight into cognitive mechanisms involved in the predisposition, precipitation, and perpetuation of tinnitus and consequent cognitive-emotional disturbances. The current CCF for tinnitus relies on evaluative conditional learning and appraisal, generating negative valence (emotional value) and arousal (cognitive value) to annoyance, distress, and distorted perception. The suggested methodology is well-defined, reproducible, and accessible, which can help foster future high-quality clinical databases. Perceived tinnitus through the perpetual-learning process can always lead to annoyance, but only in the clinical stage directly cause annoyance. In the clinical stage, tinnitus perception can lead indirectly to distress only with experiencing annoyance either with (“$${\underset{{\mathcal {C}}}{{{\varvec{Ind-1}}}}}$$ I n d - 1 C ” = 1.87; 95% CI 1.18–2.72)[“1st indirect path in the Clinical stage model”: Tinnitus Loudness $$\rightarrow$$ → Attention Bias $$\rightarrow$$ → Cognitive-Emotional Value $$\rightarrow$$ → Annoyance $$\rightarrow$$ → Clinical Distress]or without (“$${\underset{{\mathcal {C}}}{{{\varvec{Ind-2}}}}}$$ I n d - 2 C ”= 2.03; 95% CI 1.02–3.32)[ “2nd indirect path in the Clinical stage model”: Tinnitus Loudness $$\rightarrow$$ → Annoyance $$\rightarrow$$ → Clinical Distress] the perpetual-learning process. Further real-life testing of the CCF is expected to express a meticulous, decision-supporting platform for cognitive rehabilitation and clinical interventions. Furthermore, the suggested methodology offers a reliable platform for CCF development in other cognitive impairments and supports the causal clinical data models. It may also enhance our knowledge of psychological disorders and complicated comorbidities by supporting the design of different rehabilitation interventions and comprehensive frameworks in line with the “preventive medicine” policy.

show abstract

“…Thus far, differences in gender have been found to play a role in tinnitus research [ 12 , 13 , 14 , 15 ]. Women with tinnitus more frequently reported cardiovascular disease, fibromyalgia, or burnout, among others.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tinnitus Perception Based on Daily Life MHealth Data Using Country Origin and Season

Allgaier

Schlee

Probst

et al. 2022

JCM

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Predicting the gender of individuals with tinnitus based on daily life data of the TrackYourTinnitus mHealth platform

Cited by 7 publications

References 69 publications

Practical approaches in evaluating validation and biases of machine learning applied to mobile health studies

Practical approaches in evaluating validation and biases of machine learning applied to mobile health studies

Conceptual framework for tinnitus: a cognitive model in practice

Prediction of Tinnitus Perception Based on Daily Life MHealth Data Using Country Origin and Season

Contact Info

Product

Resources

About