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Karl Øyvind Mikalsen
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2020 – today
- 2024
- [j14]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. Int. J. Medical Informatics 184: 105370 (2024) - [c9]Helge Fredriksen
Categorization of phenotype trajectories utilizing transformers on clinical time-series. ICMLT 2024: 311-316 - [c8]Jørgen Aarmo Lund
Instruction-guided deidentification with synthetic test cases for Norwegian clinical text. NLDL 2024: 145-152 - [i16]Taridzo Chomutare, Therese Olsen Svenning, Miguel Angel Tejedor Hernandez, Phuong Dinh Ngo, Andrius Budrionis, Kaisa Markljung, Lill Irene Hind, Torbjørn Torsvik, Karl Øyvind Mikalsen, Aleksandar Babic, Hercules Dalianis:
Artificial intelligence to improve clinical coding practice in Scandinavia: a crossover randomized controlled trial. CoRR abs/2410.23725 (2024) - 2023
- [j13]Kristoffer Knutsen Wickstrøm
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. Comput. Medical Imaging Graph. 107: 102239 (2023) - [j12]Kristoffer K. Wickstrøm
RELAX: Representation Learning Explainability. Int. J. Comput. Vis. 131(6): 1584-1610 (2023) - [j11]Ane Blázquez-García
Selective Imputation for Multivariate Time Series Datasets With Missing Values. IEEE Trans. Knowl. Data Eng. 35(9): 9490-9501 (2023) - [i15]Helge Fredriksen, Per Joel Burman, Ashenafi Zebene Woldaregay, Karl Øyvind Mikalsen, Ståle Nymo:
Approaching adverse event detection utilizing transformers on clinical time-series. CoRR abs/2311.09165 (2023) - 2022
- [j10]Kristoffer Wickstrøm
Mixing up contrastive learning: Self-supervised representation learning for time series. Pattern Recognit. Lett. 155: 54-61 (2022) - [j9]Ahcène Boubekki
Clinically Relevant Features for Predicting the Severity of Surgical Site Infections. IEEE J. Biomed. Health Informatics 26(4): 1794-1801 (2022) - [c7]Kristoffer Wickstrøm
The Kernelized Taylor Diagram. NAIS 2022: 125-131 - [i14]Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen:
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series. CoRR abs/2203.09270 (2022) - [i13]Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen:
The Kernelized Taylor Diagram. CoRR abs/2205.08864 (2022) - [i12]Kristoffer Knutsen Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya, Karl Øyvind Mikalsen, Michael Christian Kampffmeyer, Robert Jenssen:
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. CoRR abs/2207.04812 (2022) - 2021
- [j8]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen:
Time series cluster kernels to exploit informative missingness and incomplete label information. Pattern Recognit. 115: 107896 (2021) - [j7]Kristoffer Wickstrøm
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. IEEE J. Biomed. Health Informatics 25(7): 2435-2444 (2021) - [i11]Óscar Escudero-Arnanz, Joaquín Álvarez-Rodríguez, Karl Øyvind Mikalsen, Robert Jenssen, Cristina Soguero-Ruíz:
On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit. CoRR abs/2107.10398 (2021) - [i10]Kristoffer K. Wickstrøm, Daniel J. Trosten, Sigurd Løkse, Karl Øyvind Mikalsen, Michael C. Kampffmeyer, Robert Jenssen:
RELAX: Representation Learning Explainability. CoRR abs/2112.10161 (2021) - 2020
- [i9]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Robert Jenssen:
A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs. CoRR abs/2002.12359 (2020) - [i8]Kristoffer Wickstrøm, Karl Øyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen:
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. CoRR abs/2010.11310 (2020)
2010 – 2019
- 2019
- [j6]Primoz Kocbek
Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data. Comput. Math. Methods Medicine 2019: 2059851:1-2059851:13 (2019) - [j5]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz
Noisy multi-label semi-supervised dimensionality reduction. Pattern Recognit. 90: 257-270 (2019) - [j4]Filippo Maria Bianchi
Learning representations of multivariate time series with missing data. Pattern Recognit. 96 (2019) - [i7]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Robert Jenssen:
Noisy multi-label semi-supervised dimensionality reduction. CoRR abs/1902.07517 (2019) - [i6]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen:
Time series cluster kernels to exploit informative missingness and incomplete label information. CoRR abs/1907.05251 (2019) - 2018
- [j3]Jonas Nordhaug Myhre
Robust clustering using a kNN mode seeking ensemble. Pattern Recognit. 76: 491-505 (2018) - [j2]Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruíz
Time series cluster kernel for learning similarities between multivariate time series with missing data. Pattern Recognit. 76: 569-581 (2018) - [c6]Andreas Storvik Strauman, Filippo Maria Bianchi, Karl Øyvind Mikalsen, Michael Kampffmeyer, Cristina Soguero-Ruíz
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks. BHI 2018: 307-310 - [c5]Mads A. Hansen, Karl Øyvind Mikalsen, Michael Kampffmeyer, Cristina Soguero-Ruíz
Towards deep anchor learning. BHI 2018: 315-318 - [c4]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz
Using multi-anchors to identify patients suffering from multimorbidities. BIBM 2018: 1514-1521 - [c3]Filippo Maria Bianchi, Karl Øyvind Mikalsen, Robert Jenssen:
Learning compressed representations of blood samples time series with missing data. ESANN 2018 - [i5]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen:
An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples. CoRR abs/1803.07879 (2018) - [i4]Filippo Maria Bianchi, Lorenzo Livi, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen:
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders. CoRR abs/1805.03473 (2018) - 2017
- [j1]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz
Using anchors from free text in electronic health records to diagnose postoperative delirium. Comput. Methods Programs Biomed. 152: 105-114 (2017) - [c2]Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruíz
The time series cluster kernel. MLSP 2017: 1-6 - [i3]Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruíz, Robert Jenssen:
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data. CoRR abs/1704.00794 (2017) - [i2]Filippo Maria Bianchi, Karl Øyvind Mikalsen, Robert Jenssen:
Learning compressed representations of blood samples time series with missing data. CoRR abs/1710.07547 (2017) - [i1]Andreas Storvik Strauman, Filippo Maria Bianchi, Karl Øyvind Mikalsen, Michael Kampffmeyer, Cristina Soguero-Ruíz, Robert Jenssen:
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks. CoRR abs/1711.06516 (2017) - 2015
- [c1]Jonas Nordhaug Myhre
Consensus Clustering Using kNN Mode Seeking. SCIA 2015: 175-186
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