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Førsteamanuensis Department of Clinical Medicine karl.o.mikalsen@uit.no You can find me here

Karl Øyvind Mikalsen

Job description

Associate professor at Department of Clinical Medicine.


 

Member of UiT Machine Learning Group. Please see http://machine-learning.uit.no/

 

Centre manager, SPKI Senter for pasientnær kunstig intelligens. Please see www.spki.no

 

Google scholar profile

 

  • Kjersti Mevik, Ashenafi Zebene Woldaregay, Alexander Ringdal, Karl Øyvind Mikalsen, Yuan Xu :
    Exploring surgical infection prediction: A comparative study of established risk indexes and a novel model
    International Journal of Medical Informatics 2024 DOI
  • Taridzo Fred Chomutare, Anastasios Lamproudis, Andrius Budrionis, Therese Olsen Svenning, Lill Irene Hind, Phuong Dinh Ngo et al.:
    Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial
    JMIR Research Protocols 2024 ARKIV / DOI
  • Helge Ingvart Fredriksen, Per Joel Burman Burman, Ashenafi Zebene Woldaregay, Karl Øyvind Mikalsen, Ståle Haugset Nymo :
    Categorization of phenotype trajectories utilizing transformers on clinical time-series
    Association for Computing Machinery (ACM) 2024 DOI
  • Marthe Larsen, Camilla Flåt Olstad, Christoph I. Lee, Tone Hovda, Solveig Kristin Roth Hoff, Marit Almenning Martiniussen et al.:
    Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway
    Radiology: Artificial Intelligence (RAI) 2024 ARKIV / FULLTEKST / DOI
  • Jørgen Aarmo Lund, Per Joel Burman Burman, Ashenafi Zebene Woldaregay, Robert Jenssen, Karl Øyvind Mikalsen :
    Instruction-guided deidentification with synthetic test cases for Norwegian clinical text
    Proceedings of Machine Learning Research (PMLR) 2024 ARKIV
  • Kristoffer Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
    Computerized Medical Imaging and Graphics 2023 ARKIV / DOI
  • Keyur Radiya, Henrik Lykke Joakimsen, Karl Øyvind Mikalsen, Eirik Kjus Aahlin, Rolf Ole Lindsetmo, Kim Erlend Mortensen :
    Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review
    European Radiology 2023 ARKIV / DOI
  • Ane Blazquez-Garcia, Kristoffer Knutsen Wickstrøm, Shujian Yu, Karl Øyvind Mikalsen, Ahcene Boubekki, Angel Conde et al.:
    Selective Imputation for Multivariate Time Series Datasets with Missing Values
    IEEE Transactions on Knowledge and Data Engineering 2023 ARKIV / DOI
  • Kristoffer Wickstrøm, Daniel Johansen Trosten, Sigurd Eivindson Løkse, Ahcene Boubekki, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    RELAX: Representation Learning Explainability
    International Journal of Computer Vision 2023 ARKIV / DOI
  • Marthe Larsen, Camilla Flåt Olstad, Henrik Wethe Koch, Marit Almenning Martiniussen, Solveig Kristin Roth Hoff, Håkon Lund-Hanssen et al.:
    AI Risk Score on Screening Mammograms Preceding Breast Cancer Diagnosis
    Radiology 2023 ARKIV / DATA / DOI
  • Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen :
    Mixing up contrastive learning: Self-supervised representation learning for time series
    Pattern Recognition Letters 2022 ARKIV / DOI
  • Mathias K. Hauglid, Karl Øyvind Mikalsen :
    Tilgang til helseopplysninger i maskinlæringsprosjekter
    Lov og Rett 2022 ARKIV / DOI
  • Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Eivindson Løkse, Gusatu Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    The Kernelized Taylor Diagram
    Communications in Computer and Information Science (CCIS) 2022 ARKIV / DOI
  • Ahcene Boubekki, Jonas Nordhaug Myhre, Luigi Tommaso Luppino, Karl Øyvind Mikalsen, Arthur Revhaug, Robert Jenssen :
    Clinically relevant features for predicting the severity of surgical site infections
    IEEE Journal of Biomedical and Health Informatics 2021 ARKIV / FULLTEKST / DOI
  • Karl Øyvind Mikalsen, Cristina Soguero Ruiz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen :
    Time series cluster kernels to exploit informative missingness and incomplete label information
    Pattern Recognition 2021 ARKIV / DOI
  • Kristoffer Knutsen Wickstrøm, Karl Oyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen :
    Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series
    IEEE Journal of Biomedical and Health Informatics 2021 ARKIV / DOI
  • Karl Øyvind Mikalsen, Cristina Soguero Ruiz, Robert Jenssen :
    A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs
    Springer 2020 ARKIV
  • Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi, Robert Jenssen :
    Noisy multi-label semi-supervised dimensionality reduction
    Pattern Recognition 2019 ARKIV / DOI
  • Primoz Kocbek, Nino Fijacko, Cristina Soguero Ruiz, Karl Øyvind Mikalsen, Uros Maver, Petra Povalej Brzan et al.:
    Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data
    Computational & Mathematical Methods in Medicine 2019 ARKIV / DOI
  • Filippo Maria Bianchi, Lorenzo Livi, Karl Øyvind Mikalsen, Michael C. Kampffmeyer, Robert Jenssen :
    Learning representations of multivariate time series with missing data
    Pattern Recognition 2019 ARKIV / DOI
  • Filippo Maria Bianchi, Karl Øyvind Mikalsen, Robert Jenssen :
    Learning compressed representations of blood samples time series with missing data
    2018 DOI
  • Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Inmaculada Mora-Jiménez, Isabel Caballero López Fando, Robert Jenssen :
    Using multi-anchors to identify patients suffering from multimorbidities
    IEEE (Institute of Electrical and Electronics Engineers) 2018 DOI
  • Andreas Storvik Strauman, Filippo Maria Bianchi, Karl Øyvind Mikalsen, Michael C. Kampffmeyer, Cristina Soguero-Ruiz, Robert Jenssen :
    Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks
    IEEE (Institute of Electrical and Electronics Engineers) 2018 DOI
  • Mads Adrian Hansen, Karl Øyvind Mikalsen, Michael C. Kampffmeyer, Cristina Soguero-Ruiz, Robert Jenssen :
    Towards deep anchor learning
    IEEE (Institute of Electrical and Electronics Engineers) 2018 DOI
  • Karl Øyvind Mikalsen, Cristina Soguero Ruiz, Kasper Jensen, Kristian Hindberg, Mads Gran, Arthur Revhaug et al.:
    Using anchors from free text in electronic health records to diagnose postoperative delirium
    Computer Methods and Programs in Biomedicine 2017 ARKIV / DOI
  • Kasper Jensen, Soguero-Ruiz Cristina, Karl Øyvind Mikalsen, Rolv-Ole Lindsetmo, Irene Kouskoumvekaki, Mark Girolami et al.:
    Analysis of free text in electronic health records for identification of cancer patient trajectories
    Scientific Reports 2017 ARKIV / DOI
  • Jonas Nordhaug Myhre, Robert Jenssen, Karl Øyvind Mikalsen, Sigurd Løkse :
    Robust clustering using a kNN mode seeking ensemble
    Pattern Recognition 2017 ARKIV / FULLTEKST / PROSJEKT / DOI
  • Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero Ruiz, Robert Jenssen :
    Time series cluster kernel for learning similarities between multivariate time series with missing data
    Pattern Recognition 2017 ARKIV / FULLTEKST / DOI
  • Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero Ruiz, Robert Jenssen :
    The time series cluster kernel
    IEEE Signal Processing Society 2017
  • Joel Burman, Elin Kile, Karl Øyvind Mikalsen, Samuel Kuttner :
    PET-MRI-based prediction models for classifying prostate cancer
    2024
  • Robert Jenssen, Rolf Ole Lindsetmo, Karl Øyvind Mikalsen, Oddny Johnsen :
    Markerer Tromsøs fortrinn på kunstig intelligens
    19. April 2024
  • Karl Øyvind Mikalsen, Marte Stoksvik, Karoline Skrøder, Agnethe Eltoft, Tommy Skar :
    Universitetssykehuset Nord-Norge bruker kunstig intelligens for bedre behandling av slag og blodpropp
    10. September 2024
  • Anita Schumacher, Lars Kristian Jenvin Hågensen, Karl Øyvind Mikalsen, Karl Ivar Lorentzen, Grete Hansen, Ken Inge Adolfsen et al.:
    UNN mener kunstig intelligens er veien videre: – Pasientene må ha tillit til at dette er trygt
    01. July 2023
  • Kristoffer Knutsen Wickstrøm, Daniel Johansen Trosten, Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    RELAX: Representation Learning Explainability
    2022
  • Daniel Johansen Trosten, Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    RELAX: Representation Learning Explainability
    2022
  • Kristoffer Wickstrøm, Eirik Agnalt Østmo, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    Explaining representations for medical image retrieval
    2022
  • Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Eivindson Løkse, Gusatu Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    The Kernelized Taylor Diagram
    2022
  • Sigurd Eivindson Løkse, Michael Kampffmeyer, Robert Jenssen, Karl Øyvind Mikalsen :
    Towards Explainable Representation Learning
    2021
  • Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    Towards Explainable Representation Learning
    2021
  • Kristoffer Knutsen Wickstrøm, Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    Towards Explainable Representation Learning
    2021
  • Kristoffer Knutsen Wickstrøm, Karl Øyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen :
    Uncertainty-Aware Deep Ensembles for Explainable Time Series Prediction
    2021
  • Karl Øyvind Mikalsen, Finn Henry Hansen :
    Strategi for kunstig intelligens i Helse Nord 2022-2025
    2021 FULLTEKST / OMTALE
  • Michael Kampffmeyer, Robert Jenssen, Karl Øyvind Mikalsen, Sigurd Eivindson Løkse :
    Towards Explainable Representation Learning
    2021
  • Oscar Escudero-Arnanz, Joaquín Rodríguez-Álvarez, Karl Øyvind Mikalsen, Robert Jenssen, Cristina Soguero-Ruiz :
    On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit
    2021 ARKIV / FULLTEKST
  • Michael Kampffmeyer, Robert Jenssen, Karl Øyvind Mikalsen, Arthur Revhaug :
    Uncertainty-Aware Deep Ensembles for Explainable Time Series Prediction
    2020
  • Mathias Hauglid, Karl Øyvind Mikalsen, Rolv-Ole Lindsetmo :
    Bruk av helseopplysninger i beslutningsstøtteverktøy (kunstig intelligens) - høringsuttalelse
    2020 FULLTEKST
  • Karl Øyvind Mikalsen, Robert Jenssen :
    Advancing Unsupervised and Weakly Supervised Learning with Emphasis on Data-Driven Healthcare
    UiT Norges arktiske universitet 2019
  • Jonas Myhre, Karl Øyvind Mikalsen, Sigurd Løkse, Robert Jenssen :
    Robust Non-Parametric Mode Clustering
    2016
  • Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruiz, Stein Olav Skrøvseth, Rolv-Ole Lindsetmo, Arthur Revhaug et al.:
    Learning similarities between irregularly sampled short multivariate time series from EHRs
    2016 ARKIV
  • Karl Øyvind Mikalsen, Robert Jenssen, Fred Godtliebsen, Stein Olav Skrøvseth, Arthur Revhaug, Rolv-Ole Lindsetmo et al.:
    Predicting Postoperative Delirium Using Anchors.
    2015

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