G. Harmsen

Memory performance of ARM processors and its relevance to High Energy Physics

B. Mellado, T. Wrigley, G. Harmsen, 2015 .

Harnessing feature extraction capacities from a pre-trained convolutional neural network (VGG-16) for the unsupervised distinction of aortic outflow velocity profiles in patients with severe aortic stenosis

D. Rueckert, H. Schunkert, M. Joner, 2022, European heart journal. Digital health.

Subphenotyping of Patients With Aortic Stenosis by Unsupervised Agglomerative Clustering of Echocardiographic and Hemodynamic Data.

T. Schuster, H. Schunkert, M. Joner, 2021, JACC. Cardiovascular interventions.

Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement

H. Schunkert, M. Joner, A. Kastrati, 2022, Open Heart.

Solving the Pulmonary Hypertension Paradox in Patients With Severe Tricuspid Regurgitation by Employing Artificial Intelligence.

M. Joner, S. Yuasa, K. Laugwitz, 2022, JACC. Cardiovascular interventions.

Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair.

H. Schunkert, M. Joner, A. Kastrati, 2023, European heart journal. Cardiovascular Imaging.

Kinetic and mechanistic aspects of the oxidation of chlorophenols by ozone

W. Rulkens, H. Bruning, J. Bijsterbosch, 1997 .