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Filters: 1 is biblio_type:Poster and Author is Charlotte Glinge  [Reset Search]
2021
M. Maleckar, P. M. Florvaag, V. Strøm, C. Glinge, R. Jabbari, N. Vejlstrup, T. Engstrom, K. A. Ahtarovski, T. Jespersen, J. Tfelt-Hansen et al. B-PO02-022 Combining simulation and machine learning to accurately predict arrhythmic risk in post-infarction patients In Heart Rhythm. Vol. 18. Boston, MA: Elsevier, 2021.
2019
P. M. Florvaag, V. Strøm, C. Glinge, R. Jabbari, N. Vejlstrup, T. Engstrom, K. A. Ahtarovski, T. Jespersen, J. Tfelt-Hansen, V. Naumova et al. A Combined In-Silico and Machine Learning Approach towards Predicting Arrhythmic Risk in Post-Infarction Patients In Computing in Cardiology, Singapore., 2019.
2017
M. Marciniak, H. Arevalo, J. Tfelt-Hansen, K. A. Ahtarovski, T. Jespersen, R. Jabbari, C. Glinge, N. Vejlstrup, T. Engstrom, M. Maleckar et al. A multiple kernel learning framework to investigate the relationship between ventricular fibrillation and first myocardial infarction In Functional Imaging and Modelling of the Heart, Edited by M. Pop and G. Wright. Springer, 2017.
2016
M. Marciniak, H. Arevalo, J. Tfelt-Hansen, T. Jespersen, R. Jabbari, C. Glinge, K. A. Ahtarovski, N. Vejlstrup, M. Maleckar and K. S. Mcleod. From MR image to patient-specific simulation and population-based analysis:Tutorial for an openly available image-processing pipeline In MICCAI Workshop on Statistical Atlases and Cardiac Models of the Heart, Edited by T. Engstrom. Berlin: Springer, 2016.