| 11 |
Predicting comorbidities of epilepsy patients using big data from Electronic Health Records augmented with biomedical knowledge |
Thomas Linden ( Fraunhofer SCAI, Bonn-Aachen International Center for Information Technology ) |
| 12 |
Identifying, predicting and validating subtypes of Parkinson Disease progression using machine learning |
Ashar Ahmad(University of Bonn) |
| 13 |
Generative Artificial Intelligence Approaches for Modeling of Multimodal Longitudinal Clinical Studies and Simulation of Virtual Cohorts |
Philipp Wendland (Fraunhofer SCAI, University of Applied Sciences Koblenz) |
| 14 |
Machine learning classification of Alzheimer’s disease: Diagnostic Prediction Using Cognitive and Functional Domains |
Mohamed Aborageh (Fraunhofer SCAI) |
| 15 |
Robust prediction of age from MEG/EEG signals without biophysical source modeling |
David Sabbagh (INRIA) |
| 16 |
Lack of support for a common cause hypothesis of visuo-cognitive aging: multivariate statistical analysis on the SilverSight follow-up cohort study |
Angelo Arleo (CNRS Vision Institute) |
| 17 |
Deep learning models for brain age estimation |
Lara Dular (University of Ljubljana, Faculty of Electrical Engineering, Laboratory of Imaging Technologies) |
| 18 |
The scored events model: Subtype and Stage Inference (SuStaIn) for visual ratings, clinical scores and other ordinal data |
Alexandra Young (Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King′s College London) |
| 19 |
Modelling longitudinal binary data in Parkinson |
Pierre-Emmanuel Poulet (ARAMIS Lab) |
| 20 |
Modeling the progression of Parkinson’s Disease : comparison of subjects with and without Sleep Disorders |
Raphaël Couronné (UPMC/INRIA) |
| 21 |
Predicted brain age as a cognitive biomarker in Multiple Sclerosis |
Stijn Denissen (UZ Brussels/icometrix) |