In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.
01
Kopar, S., Rane, R.P., Mychajliw, C., Federmann, L., Eschweiler, G., Berg, D., Gijsen, S., Perez-Toro, P.A. & Ritter, K.,
Beyond Binary: Speech Representations Across the Cognitive Score Hierarchy.
02
Hammelrath, L., Rane, R. P., Gijsen, S., Jüres, F., Brose, A., Ritter, K., ... & Knaevelsrud, C.
Comparing personalized and population-based models for predicting momentary negative affect in internalizing disorders: A digital phenotyping study
03
Gijsen, S., Schulz, M. A., & Ritter, K.
Brain-Semantoks: Learning Semantic Tokens of Brain Dynamics with a Self-Distilled Foundation Model.
04
Zvarova, P., van der Linden, C., Li, N., Butenko, K., Berger, T., Meyer, G.M., Sahin, I.A., Goede, L.L., Bahners, B.H., Hollunder, B. and Dembek, T.A., ..., Ritter K., ... & Horn, A.
Multimodal Image Guidance in Subthalamic Deep Brain Stimulation for Parkinson's Disease.
05
Erk, S., Wellan, S., Henze, G.I., Ritter, K., Lueken, U. and Walter, H.
Präzisionspsychiatrie und-psychotherapie: Was ist das?
06
Ritter, K., Brandt, L., & Walter, H.
KI in der Psychiatrie
07
Heinrichs, B., Diegelmann, D., Friedrich, O., Heinrichs, J.H., Kellmeyer, P., Madai, V.I., Mandelartz, S., Nähr-Wagener, S., Namuganza, S., Ritter, K. and Schleidgen, S.