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Machine Learning for Medical Data Analysis

A stethoscope placed on a medical form.
Photo: Colourbox.de

Machine learning can help solving diagnostic and prognostic problems in a variety of medical domains.
We have developed machine learning approaches for recommending algorithms to predict drug sensitivity to cancer therapy, predicting epileptic seizures based on features extracted from EEG data, detecting depressive episodes using mobile devices, and improving data quality in disease registries. Furthermore, we have proposed several approaches for segmenting objects in medical images.

Selected Publications

  • Salma Daoud, Afef Mdhaffar, Mohamed Jmaiel, Bernd Freisleben:
    Q-Rank: Reinforcement Learning for Recommending Algorithms to Predict Drug Sensitivity to Cancer Therapy. IEEE Journal of Biomedical and Health Informatics 24(11): 3154-3161, 2020
  • Abir Affes, Afef Mdhaffar, Chahnez Triki, Mohamed Jmaiel, Bernd Freisleben:
    A Convolutional Gated Recurrent Neural Network for Epileptic Seizure Prediction. 17th International Conference on Smart Living and Public Health, New York City, NY, USA, LNCS 11862, 85-96, Springer, 2019
  • Afef Mdhaffar, Fedi Cherif, Yousri Kessentini, Manel Maalej, Jihen Ben Thabet, Mohamed Maalej, Mohamed Jmaiel, Bernd Freisleben:
    DL4DED: Deep Learning for Depressive Episode Detection on Mobile Devices. 17th International Conference on Smart Living and Public Health, New York City, NY, USA, LNCS 11862, 109-121, Springer, 2019
  • Hatem Bellaaj, Afef Mdhaffar, Mohamed Jmaiel, Sondes Hdiji Mseddi, Bernd Freisleben:
    An Adaptive Neuro-Fuzzy Inference System for Improving Data Quality in Disease Registries. 33rd Annual ACM Symposium on Applied Computing, Pau, France, 30-33, ACM, 2018
  • Salma Daoud, Afef Mdhaffar, Bernd Freisleben, Mohamed Jmaiel:
    A Multi-Criteria Decision Making Approach for Predicting Cancer Cell Sensitivity to Drugs. 33rd Annual ACM Symposium on Applied Computing, Pau, France, 47-53, ACM, 2018
  • R. Schwarzenberg, B. Freisleben, C. Nimsky, J. Egger:
    Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences. PLoS ONE 9(4): e93389, 2014
  • Jan Egger, Tobias Lüddemann, Robert Schwarzenberg, Bernd Freisleben, Christopher Nimsky:
    Interactive-cut: Real-time Feedback Segmentation for Translational Research. Computerized Medical Imaging and Graphics 38(4): 285-295, 2014
  • Jan Egger, Stefan Grosskopf, Christopher Nimsky, Tina Kapur, Bernd Freisleben:
    Modeling and Visualization Techniques for Virtual Stenting of Aneurysms and Stenoses. Computerized Medical Imaging and Graphics 36(3): 183-203, 2012

Further information

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