Bilde av Mikalsen, Karl Øyvind
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Bilde av Mikalsen, Karl Øyvind
Førsteamanuensis Department of Clinical Medicine karl.o.mikalsen@uit.no Tromsø You can find me here

Karl Øyvind Mikalsen


Job description

Jeg er førsteamanuensis ved Institutt for klinisk medisin og medlem av maskinlæringsgruppa på UiT. Hovedstillingen min er at jeg er avdelingsleder ved Senter for pasientnær kunstig intelligens på UNN.  Min bakgrunn er at jeg har en doktorgrad i medisinsk KI.

I min forskning jobber jeg med utvikling av KI-programmer for å analysere medisinske bilder, tale og tekst. I denne forskningen benyttes både språkmodeller (generativ KI) og tradisjonelle maskinlæringsalgoritmer.

Jeg forsker også på innføring av kommersielt utviklede KI-verktøy i helsetjenesten. Forskningen består hovedsaklig i å gjennomføre ulike former for kliniske studier med bruk av KI; alt i fra små gjennomførbarhetsstudier til store randomiserte kontrollerte studier (RCTer).

Forskningen min er tverrfaglig og gjøres i samarbeid mellom ulike fagmiljøer på UiT og UNN.

 


  • 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
  • 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) 10. abril 2024 ARKIV / FULLTEKST / DOI
  • 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 ARKIV / 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 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, 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
  • 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
  • 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
  • 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
  • Robert Jenssen, Rolf Ole Lindsetmo, Karl Øyvind Mikalsen, Oddny Johnsen :
    Markerer Tromsøs fortrinn på kunstig intelligens
    19. abril 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. septiembre 2024
  • Joel Burman, Elin Kile, Karl Øyvind Mikalsen, Samuel Kuttner :
    PET-MRI-based prediction models for classifying prostate cancer
    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. julio 2023
  • 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
  • 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
  • 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
  • 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
  • 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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    Publikasjoner utenom Cristin

    Mine publikasjoner finner du her: Google scholar profile 


    Research interests

    Utvikling og bruk av kunstig intelligens innen medisin og helse.