Karl Øyvind Mikalsen

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Robert JenssenVisual Intelligence, UiT The Arctic University of Norway & Norw. Comp. Center & P1 Centre AI, UCPHZweryfikowany adres z uit.no
Cristina Soguero RuizAssociate Professor URJCZweryfikowany adres z urjc.es
Michael Christian KampffmeyerProfessor in Machine Learning, UiT the Arctic University of Norway, TromsøZweryfikowany adres z post.uit.no
Filippo Maria BianchiUiT the Arctic University of Norway - Dept. of Mathematics and StatisticsZweryfikowany adres z uit.no
Kristoffer Knutsen WickstrømAssociate professor in machine learning at UiT The Arctic University of NorwayZweryfikowany adres z uit.no
Stein Olav SkrøvsethNorwegian Centre for E-health ResearchZweryfikowany adres z ehealthresearch.no
Sigurd LøkseResearch Scientist at NORCE Norwegian Research CentreZweryfikowany adres z norceresearch.no
Rolv-Ole LindsetmoUniversitetet i TromsøZweryfikowany adres z unn.no
Jonas Nordhaug MyhreSenior Researcher, Northern Research Institute NORCEZweryfikowany adres z uit.no
Ahcène BoubekkiPhysikalisch-Technische Bundesanstalt, Berlin, GermanyZweryfikowany adres z ptb.de
Lorenzo LiviUniversity of Manitoba and University of ExeterZweryfikowany adres z umanitoba.ca
Irene KouskoumvekakiTechnical University of DenmarkZweryfikowany adres z cbs.dtu.dk
Mark GirolamiUniversity of CambridgeZweryfikowany adres z cam.ac.uk
Andraz StozerFaculty of Medicine, University of Maribor, Slovenia; Center for Open Innovation and ResearchZweryfikowany adres z um.si
Gregor StiglicProfessor, University of Maribor, Faculty of Health SciencesZweryfikowany adres z um.si
Nino FijačkoUM FZVZweryfikowany adres z um.si
Uroš Maverassociate professor, Faculty of Medicine, University of MariborZweryfikowany adres z um.si
Petra Povalej BrzanAssistant Professor, Faculty of Medicine & Faculty of Electrical Engineering and Computer ScienceZweryfikowany adres z um.si
Primoz KocbekUniversity of Maribor, Faculty of Health SciencesZweryfikowany adres z um.si
Daniel J. TrostenUiT - The Arctic University of NorwayZweryfikowany adres z uit.no

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Karl Øyvind Mikalsen

University Hospital of North Norway & UiT The Arctic University of Norway, Tromsø

Zweryfikowany adres z uit.no

Machine learning deep learning artificial intelligence medical AI health data analytics


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
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), 46226, 2017	151	2017
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	118	2018
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	103	2022
Learning representations of multivariate time series with missing data FM Bianchi, L Livi, KØ Mikalsen, M Kampffmeyer, R Jenssen Pattern Recognition 96, 106973, 2019	81*	2019
Robust clustering using a kNN mode seeking ensemble JN Myhre, KØ Mikalsen, S Løkse, R Jenssen Pattern Recognition 76, 491-505, 2018	55	2018
Noisy multi-label semi-supervised dimensionality reduction KØ Mikalsen, C Soguero-Ruiz, FM Bianchi, R Jenssen Pattern Recognition 90, 257-270, 2019	41	2019
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	39	2020
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	28	2018
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 (1), 2059851, 2019	23	2019
Relax: Representation learning explainability KK Wickstrøm, DJ Trosten, S Løkse, A Boubekki, K Mikalsen, ... International Journal of Computer Vision, 1-27, 2023	20	2023
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	17	2017
Learning similarities between irregularly sampled short multivariate time series from EHRs KØ Mikalsen, FM Bianchi, C Soguero-Ruiz, SO Skrøvseth, RO Lindsetmo, ...	12	2016
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	10	2021
Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review K Radiya, HL Joakimsen, KØ Mikalsen, EK Aahlin, RO Lindsetmo, ... European Radiology 33 (10), 6689-6717, 2023	9	2023
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images KK Wickstrøm, EA Østmo, K Radiya, KØ Mikalsen, MC Kampffmeyer, ... Computerized Medical Imaging and Graphics 107, 102239, 2023	9	2023
AI risk score on screening mammograms preceding breast cancer diagnosis M Larsen, CF Olstad, HW Koch, MA Martiniussen, SR Hoff, ... Radiology 309 (1), e230989, 2023	7	2023
Selective imputation for multivariate time series datasets with missing values A Blázquez-García, K Wickstrøm, S Yu, KØ Mikalsen, A Boubekki, ... IEEE Transactions on Knowledge and Data Engineering 35 (9), 9490-9501, 2023	7	2023
Learning compressed representations of blood samples time series with missing data FM Bianchi, KØ Mikalsen, R Jenssen arXiv preprint arXiv:1710.07547, 2017	7	2017
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	6	2018
Consensus clustering using knn mode seeking JN Myhre, KØ Mikalsen, S Løkse, R Jenssen Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark …, 2015	6	2015

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