Thilo Rieg

From mechanistic insight towards clinical implementation using normative modeling

E. Schwarz, Thilo Rieg, 2022, Nature Computational Science.

dsMTL: a computational framework for privacy-preserving, distributed multi-task machine learning

Julian O. Matschinske, P. Burton, D. Dwyer, 2021, bioRxiv.

High-performance detection of alcoholism by unfolding the amalgamated EEG spectra using the Random Forests method

Ricardo Buettner, Janek Frick, Thilo Rieg, 2019, Hawaii International Conference on System Sciences.

Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms

Hermann Baumgartl, Ricardo Buettner, Janek Frick, 2020, PloS one.

High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands

Ricardo Buettner, Janek Frick, Thilo Rieg, 2019, ICIS.

Detection of schizophrenia: A machine learning algorithm for potential early detection and prevention based on event-related potentials

Ricardo Buettner, Janek Frick, Thilo Rieg, 2021, HICSS.

Machine Learning Based Diagnosis of Diseases Using the Unfolded EEG Spectra: Towards an Intelligent Software Sensor

Ricardo Buettner, Janek Frick, Thilo Rieg, 2019, Information Systems and Neuroscience.

Machine Learning-Based Diagnosis of Epilepsy in Clinical Routine: Lessons Learned from a Retrospective Pilot Study

Ricardo Buettner, Janek Frick, Thilo Rieg, 2020 .

dsMTL: a computational framework for privacy-preserving, distributed multi-task machine learning

Julian O. Matschinske, P. Burton, D. Dwyer, 2022, Bioinform..