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

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

doi:10.3390/jcm11154270

Cited by 8 publications

(7 citation statements)

References 53 publications

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“…First, the prediction is performed on the basis of the assessment level. In other work, it could be shown what a difference this can make with respect to the user level 17 , 22 . Nevertheless, we are currently comparing the user and assessment level with the various options in a larger study.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, the question will be answered to what extent the questions of the EMA-D questionnaire that do not assess the tinnitus experience per se (i.e., loudness and distress of the tinnitus) can be used to predict the answer to the first question, whether someone is aware of the tinnitus at a particular point in time. In addition, for the first time, the study will take into account another circumstance that has been ignored in most of the previous TYT analyses (e.g., 16,17 : one problem with open-label observational studies using MCS and EMA such as TYT is that the frequency of responses from participating users varies widely 18,19 ). There are many reasons for this.…”

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confidence: 99%

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Predicting the presence of tinnitus using ecological momentary assessments

Breitmayer

Stach

Allgaier

et al. 2023

Sci Rep

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Mobile applications have gained popularity in healthcare in recent years. These applications are an increasingly important pillar of public health care, as they open up new possibilities for data collection and can lead to new insights into various diseases and disorders thanks to modern data analysis approaches. In this context, Ecological Momentary Assessment (EMA) is a commonly used research method that aims to assess phenomena with a focus on ecological validity and to help both the user and the researcher observe these phenomena over time. One phenomenon that benefits from this capability is the chronic condition tinnitus. TrackYourTinnitus (TYT) is an EMA-based mobile crowdsensing platform designed to provide more insight into tinnitus by repeatedly assessing various dimensions of tinnitus, including perception (i.e., perceived presence). Because the presence of tinnitus is the dimension that is of great importance to chronic tinnitus patients and changes over time in many tinnitus patients, we seek to predict the presence of tinnitus based on the not directly related dimensions of mood, stress level, arousal, and concentration level that are captured in TYT. In this work, we analyzed a dataset of 45,935 responses to a harmonized EMA questionnaire using different machine learning techniques. In addition, we considered five different subgroups after consultation with clinicians to further validate our results. Finally, we were able to predict the presence of tinnitus with an accuracy of up to 78% and an AUC of up to 85.7%.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Predicting the presence of tinnitus using ecological momentary assessments

Breitmayer

Stach

Allgaier

et al. 2023

Sci Rep

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“…This includes identifying tinnitus subtypes and predicting treatment outcomes. Various machine learning models, including Random Forests (Bromis et al, 2022 ) and Gradient Boosting Machines (Allgaier et al, 2022 ), have been used for pattern recognition and prediction in tinnitus data. There is emerging research on the development of automated audiometry systems using machine learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial intelligence approaches for tinnitus diagnosis: leveraging high-frequency audiometry data for enhanced clinical predictions

Sadegh-Zadeh,

Soleimani Mamalo,

Kavianpour

et al. 2024

Front. Artif. Intell.

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This research investigates the application of machine learning to improve the diagnosis of tinnitus using high-frequency audiometry data. A Logistic Regression (LR) model was developed alongside an Artificial Neural Network (ANN) and various baseline classifiers to identify the most effective approach for classifying tinnitus presence. The methodology encompassed data preprocessing, feature extraction focused on point detection, and rigorous model evaluation through performance metrics including accuracy, Area Under the ROC Curve (AUC), precision, recall, and F1 scores. The main findings reveal that the LR model, supported by the ANN, significantly outperformed other machine learning models, achieving an accuracy of 94.06%, an AUC of 97.06%, and high precision and recall scores. These results demonstrate the efficacy of the LR model and ANN in accurately diagnosing tinnitus, surpassing traditional diagnostic methods that rely on subjective assessments. The implications of this research are substantial for clinical audiology, suggesting that machine learning, particularly advanced models like ANNs, can provide a more objective and quantifiable tool for tinnitus diagnosis, especially when utilizing high-frequency audiometry data not typically assessed in standard hearing tests. The study underscores the potential for machine learning to facilitate earlier and more accurate tinnitus detection, which could lead to improved patient outcomes. Future work should aim to expand the dataset diversity, explore a broader range of algorithms, and conduct clinical trials to validate the models' practical utility. The research highlights the transformative potential of machine learning, including the LR model and ANN, in audiology, paving the way for advancements in the diagnosis and treatment of tinnitus.

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“…subjects, patients, persons ) that are represented several times within our dataset, but with a varying number of assessments per user. From previous work, we already know that so-called power-users with many more assessments than most of the other users have a high impact on the models training procedure 15 . We would further like to investigate whether a simple heuristic can outperform complex ensemble methods.…”

Section: Introductionmentioning

confidence: 99%

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

Allgaier,

Pryss

2024

Commun Med

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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.

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

Cited by 8 publications

References 53 publications

Predicting the presence of tinnitus using ecological momentary assessments

Predicting the presence of tinnitus using ecological momentary assessments

Artificial intelligence approaches for tinnitus diagnosis: leveraging high-frequency audiometry data for enhanced clinical predictions

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

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