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Karl Øyvind Mikalsen
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Analysis of free text in electronic health records for identification of cancer patient trajectories
K Jensen, C Soguero-Ruiz, K Oyvind Mikalsen, RO Lindsetmo, ...
Scientific reports 7 (1), 1-12, 2017
902017
Time series cluster kernel for learning similarities between multivariate time series with missing data
KØ Mikalsen, FM Bianchi, C Soguero-Ruiz, R Jenssen
Pattern Recognition 76, 569-581, 2018
892018
Learning representations of multivariate time series with missing data
FM Bianchi, L Livi, KØ Mikalsen, M Kampffmeyer, R Jenssen
Pattern Recognition 96, 106973, 2019
46*2019
Robust clustering using a kNN mode seeking ensemble
JN Myhre, KØ Mikalsen, S Løkse, R Jenssen
Pattern Recognition 76, 491-505, 2018
382018
Noisy multi-label semi-supervised dimensionality reduction
KØ Mikalsen, C Soguero-Ruiz, FM Bianchi, R Jenssen
Pattern Recognition 90, 257-270, 2019
272019
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks
AS Strauman, FM Bianchi, KØ Mikalsen, M Kampffmeyer, C Soguero-Ruiz, ...
2018 IEEE EMBS International Conference on Biomedical & Health Informatics …, 2018
232018
Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative …
P Kocbek, N Fijacko, C Soguero-Ruiz, KØ Mikalsen, U Maver, ...
Computational and Mathematical Methods in Medicine 2019, 2019
142019
Using anchors from free text in electronic health records to diagnose postoperative delirium
KØ Mikalsen, C Soguero-Ruiz, K Jensen, K Hindberg, M Gran, ...
Computer Methods and Programs in Biomedicine 152, 105-114, 2017
122017
Learning similarities between irregularly sampled short multivariate time series from EHRs
KØ Mikalsen, FM Bianchi, C Soguero-Ruiz, SO Skrøvseth, RO Lindsetmo, ...
92016
Time series cluster kernels to exploit informative missingness and incomplete label information
KØ Mikalsen, C Soguero-Ruiz, FM Bianchi, A Revhaug, R Jenssen
Pattern Recognition, 107896, 2021
72021
Uncertainty-aware deep ensembles for reliable and explainable predictions of clinical time series
K Wickstrøm, KØ Mikalsen, M Kampffmeyer, A Revhaug, R Jenssen
IEEE Journal of Biomedical and Health Informatics 25 (7), 2435-2444, 2020
72020
An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples
KØ Mikalsen, C Soguero-Ruiz, FM Bianchi, A Revhaug, R Jenssen
arXiv preprint arXiv:1803.07879, 2018
62018
Learning compressed representations of blood samples time series with missing data
FM Bianchi, KØ Mikalsen, R Jenssen
arXiv preprint arXiv:1710.07547, 2017
62017
Consensus clustering using knn mode seeking
JN Myhre, KØ Mikalsen, S Løkse, R Jenssen
Scandinavian Conference on Image Analysis, 175-186, 2015
62015
Mixing up contrastive learning: Self-supervised representation learning for time series
K Wickstrøm, M Kampffmeyer, KØ Mikalsen, R Jenssen
Pattern Recognition Letters 155, 54-61, 2022
12022
A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs
KØ Mikalsen, C Soguero-Ruiz, R Jenssen
Explainable AI in Healthcare and Medicine, 23-36, 2021
12021
Towards deep anchor learning
MA Hansen, KØ Mikalsen, M Kampffmeyer, C Soguero-Ruiz, R Jenssen
2018 IEEE EMBS International Conference on Biomedical & Health Informatics …, 2018
12018
Deforming the vacuum. On the physical origin and numerical calculation of the Casimir effect.
KØ Mikalsen
UiT Norges arktiske universitet, 2014
12014
The Kernelized Taylor Diagram
K Wickstrøm, JE Johnson, S Løkse, G Camps-Valls, KØ Mikalsen, ...
arXiv preprint arXiv:2205.08864, 2022
2022
RELAX: Representation Learning Explainability
KK Wickstrøm, DJ Trosten, S Løkse, KØ Mikalsen, MC Kampffmeyer, ...
arXiv preprint arXiv:2112.10161, 2021
2021
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Artikler 1–20