Publications for Data Mining in Tinnitus Research
- The statistical analysis plan for the unification of treatments and interventions for tinnitus patients randomized clinical trial (UNITI-RCT). Trials, (24)1:472, Springer, 2023.
- Parsimonious predictors for medical decision support: Minimizing the set of questionnaires used for tinnitus outcome prediction. Expert Systems with Applications, (239):122336, Elsevier BV, April 2023. URL
- Dimensions of Tinnitus-Related Distress. Brain Sciences, (12)22022. URL
- Juxtaposing Medical Centers Using Different Questionnaires Through Score Predictors. In Andreas K. Maier (Eds.), Frontiers in Neuroscience, (16)Frontiers Media SA, March 2022. URL
- User-centric vs whole-stream learning for EMA prediction. 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), 307-312, June 2021.
- Discovery of Patient Phenotypes through Multi-layer Network Analysis on the Example of Tinnitus. 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 1--10, IEEE, 2021. URL
- Love thy Neighbours: A Framework for Error-Driven Discovery of Useful Neighbourhoods for One-Step Forecasts on EMA data. 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), 295-300, June 2021.
- Circadian Conditional Granger Causalities on Ecological Momentary Assessment Data from an mHealth App. 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), 354-359, 2021.
- Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users. Entropy, (23)122021. URL
- Phenotyping chronic tinnitus patients using self-report questionnaire data: cluster analysis and visual comparison. Scientific Reports, (10)1:16411, 2020. URL
- Gender-Specific Differences in Patients With Chronic Tinnitus—Baseline Characteristics and Treatment Effects. Frontiers in Neuroscience, (14):487, 2020. URL
- The Effect of Non-Personalised Tips on the Continued Use of Self-Monitoring mHealth Applications. Brain Sciences, (10)122020. URL
- Tinnitus-related distress after multimodal treatment can be characterized using a key subset of baseline variables. PLOS ONE, (15)1:1-18, Public Library of Science, January 2020. URL
- Understanding adherence to the recording of ecological momentary assessments in the example of tinnitus monitoring. Scientific Reports, (10)1Springer Science and Business Media LLC, December 2020. URL
- Development and internal validation of a depression severity prediction model for tinnitus patients based on questionnaire responses and socio-demographics. Scientific Reports, (10)1:4664, 2020. URL
- Editorial: Towards an Understanding of Tinnitus Heterogeneity. Frontiers in aging neuroscience, (11)2019.
- Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus--stress associations based on the TrackYourTinnitus mobile platform. International Journal of Data Science and Analytics, (8)4:327--338, Nov 1, 2019. URL
- Applying Machine Learning on the Daily Life Data of the TrackYourTinnitus mHealth Crowdsensing Platform Predicts the Mobile Operating System with High Accuracy. 2019.
- Patient Empowerment Through Summarization of Discussion Threads on Treatments in a Patient Self-help Forum. In Nicos Maglaveras, Ioanna Chouvarda, and Paulo de Carvalho (Eds.), Precision Medicine Powered by pHealth and Connected Health: ICBHI 2017, Thessaloniki, Greece, 18-21 November 2017, 229-233, Springer Singapore, 2018. URL
- Studying the Potential of Multi-Target Classification on Patient Screening Data to Predict Dropout Cases. IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), 239 -243, 2018.
- Finding Tinnitus Patients with Similar Evolution of their Ecological Momentary Assessments. Proc. of the 31th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS18), 112-117, 2018.
- Review of Smart Services for Tinnitus Self-Help, Diagnostics and Treatments. Frontiers in neuroscience, (12)2018.
- Differences between Android and iOS Users of the TrackYourTinnitus Mobile Crowdsensing mHealth Platform. IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), 411 -416, 2018.
- Studying the Potential of Multi-Target Classification on Patient Screening Data to Predict Dropout Cases. Proc. of the 31th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS18), 239-243, 2018.
- Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus–stress associations based on the TrackYourTinnitus mobile platform. International Journal of Data Science and Analytics, (8):327 -338, 2018.
- Mobile Crowdsensing for the Juxtaposition of Realtime Assessments and Retrospective Reporting for Neuropsychiatric Symptoms. Proc. of IEEE Symposium on Computer-Based Medical Systems (CBMS 2017), Thessaloniki, Greece, June 2017.
- Outpatient Tinnitus Clinic, Self-Help Web Platform, or Mobile Application to Recruit Tinnitus Study Samples?. Frontiers in Aging Neuroscience, (9):113, 2017. URL
- Does Tinnitus Depend on Time-of-Day? An Ecological Momentary Assessment Study with the ``TrackYourTinnitus'' Application. Frontiers in Aging Neuroscience, (9):253, 2017. URL
- Studying the Potential of Multi-Target Classification to Characterize Combinations of Classes with Skewed Distribution. Proc. of IEEE Symposium on Computer-Based Medical Systems, Thessaloniki, Greece, 2017.