Schleicher, Miro

2023

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.

Prediction meets time series with gaps: User clusters with specific usage behavior patterns. Artificial Intelligence in Medicine, 102575, Elsevier BV, May 2023. URL

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

2022

Expect the gap: A recommender approach to estimate the absenteeism of self-monitoring mHealth app users. 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), 1-10, October 2022. URL

When Can I Expect the mHealth User to Return? Prediction Meets Time Series with Gaps. In Martin Michalowski, Syed Sibte Raza Abidi, and Samina Abidi (Eds.), Artificial Intelligence in Medicine, 310--320, Springer International Publishing, 2022.

Prediction of declining engagement to self-monitoring apps on the example of tinnitus mHealth data. 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), 228-233, July 2022. 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

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

Towards a unification of treatments and interventions for tinnitus patients: The EU research and innovation action UNITI. Progress in brain research, (260):441—451, 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.

2020

The Effect of Non-Personalised Tips on the Continued Use of Self-Monitoring mHealth Applications. Brain Sciences, (10)122020. URL

Active feature acquisition on data streams under feature drift.. Ann. des Télécommunications, (75)9-10:597-611, 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

Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from?. In Annalisa Appice, Grigorios Tsoumakas, Yannis Manolopoulos, and Stan Matwin (Eds.), DS, (12323):659-673, Springer, 2020. URL

Active feature acquisition on data streams under feature drift. Annals of Telecommunications, Jul 8, 2020. URL

2017

ICE: Interactive Classification Rule Exploration on Epidemiological Data. Proc. of the 30th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS17), 606-611, Thessaloniki, Greece, 2017.

Last Modification: 28.10.2021 - Contact Person: