English Intern
Lehrstuhl für klinische Epidemiologie und Biometrie

Johannes Allgaier

kurzer Lebenslauf

seit 2020 Wissenschaftlicher Mitarbeiter am Institut für Klinische Epidemiologie und Biometrie an der Universität Würzburg
2017-2020 Studium M.Sc. Wirtschaftswissenschaften an der Universität Ulm und National Taiwan University of Science and Technology
2014-2017 Studium B.Sc. Wirtschaftswissenschaften an der Universität Ulm


wissenschaftliche Schwerpunkte

Machine Learning, Data Science

Hauptpublikationen der letzten Jahre

2023[ to top ]
  • 1.
    Breitmayer M, Stach M, Kraft R, Allgaier J, Reichert M, Schlee W, et al. Predicting the presence of tinnitus using ecological momentary assessments. Scientific Reports [Internet]. 2023;13(1):8989. Available from: https://doi.org/10.1038/s41598-023-36172-7
  • 1.
    Beierle F, Allgaier J, Stupp C, Keil T, Schlee W, Schobel J, et al. Self-Assessment of Having COVID-19 With the Corona Check Mhealth App. IEEE J Biomed Health Inform. 2023;Pp.
2022[ to top ]
  • 1.
    Allgaier J, Schlee W, Probst T, Pryss R. Prediction of Tinnitus Perception Based on Daily Life MHealth Data Using Country Origin and Season. Journal of Clinical Medicine [Internet]. 2022;11(15):4270. Available from: https://www.mdpi.com/2077-0383/11/15/4270
2021[ to top ]
  • 1.
    Schlee W, Langguth B, Pryss R, Allgaier J, Mulansky L, Vogel C, et al. Using Big Data to Develop a Clinical Decision Support System for Tinnitus Treatment. In: Searchfield GD, Zhang J, editors. The Behavioral Neuroscience of Tinnitus [Internet]. Cham: Springer International Publishing; 2021. pp. 175-89. Available from: https://doi.org/10.1007/7854_2021_229
  • 1.
    Schlee W, Schoisswohl S, Staudinger S, Schiller A, Lehner A, Langguth B, et al. Towards a unification of treatments and interventions for tinnitus patients: The EU research and innovation action UNITI. In: Schlee W, Langguth B, Kleinjung T, Vanneste S, De Ridder D, editors. Progress in Brain Research [Internet]. Elsevier; 2021. pp. 441-5. Available from: https://www.sciencedirect.com/science/article/pii/S0079612320302351
  • 1.
    Fleischer A, Heimeshoff L, Allgaier J, Jordan K, Gelbrich G, Pryss R, et al. Is PFS the Right Endpoint to Assess Outcome of Maintenance Studies in Multiple Myeloma? Results of a Patient Survey Highlight Quality-of-Life As an Equally Important Outcome Measure. Blood [Internet]. 2021;138:836. Available from: https://www.sciencedirect.com/science/article/pii/S000649712102824X
  • 1.
    Beierle F, Schobel J, Vogel C, Allgaier J, Mulansky L, Haug F, et al. Corona Health—A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic. International Journal of Environmental Research and Public Health. 2021;18(14):7395.
  • 1.
    Allgaier J, Schlee W, Langguth B, Probst T, Pryss R. Predicting the gender of individuals with tinnitus based on daily life data of the TrackYourTinnitus mHealth platform. Scientific Reports [Internet]. 2021;11(1):18375. Available from: https://doi.org/10.1038/s41598-021-96731-8
  • 1.
    Allgaier J, Neff P, Schlee W, Schoisswohl S, Pryss R. Deep Learning End-to-End Approach for the Prediction of Tinnitus based on EEG Data. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021. pp. 816-9.
  • 1.
    Landauer J, Hoppenstedt B, Allgaier J. Image Segmentation To Locate Ancient Maya Architectures Using Deep Learning. In: Kocev D, Simidjievski N, Kostovska A, Dimitrovski I, Kokalj Z, editors. Discover the mysteries of the maya. Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia; 2021. p. 7.
2019[ to top ]
  • 1.
    Kammerer K, Hoppenstedt B, Pryss R, Stökler S, Allgaier J, Reichert M. Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings. Sensors [Internet]. 2019;19(24):5370. Available from: https://www.mdpi.com/1424-8220/19/24/5370