no code implementations • 15 Nov 2023 • Helge Fredriksen, Per Joel Burman, Ashenafi Woldaregay, Karl Øyvind Mikalsen, Ståle Nymo
Patients being admitted to a hospital will most often be associated with a certain clinical development during their stay.
2 code implementations • 11 Jul 2022 • Kristoffer Knutsen Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya, Karl Øyvind Mikalsen, Michael Christian Kampffmeyer, Robert Jenssen
We address these limitations by (1) proposing a self-supervised learning framework that incorporates domain-knowledge into the training procedure and (2) providing the first representation learning explainability analysis in the context of CBIR of CT liver images.
1 code implementation • 18 May 2022 • Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen
Our proposed kernelized Taylor diagram is capable of visualizing similarities between populations with minimal assumptions of the data distributions.
1 code implementation • 17 Mar 2022 • Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen
The lack of labeled data is a key challenge for learning useful representation from time series data.
1 code implementation • 19 Dec 2021 • Kristoffer K. Wickstrøm, Daniel J. Trosten, Sigurd Løkse, Ahcène Boubekki, Karl Øyvind Mikalsen, Michael C. Kampffmeyer, Robert Jenssen
Our approach can also model the uncertainty in its explanations, which is essential to produce trustworthy explanations.
no code implementations • 7 Jul 2021 • Óscar Escudero-Arnanz, Joaquín Rodríguez-Álvarez, Karl Øyvind Mikalsen, Robert Jenssen, Cristina Soguero-Ruiz
The acquisition of Antimicrobial Multidrug Resistance (AMR) in patients admitted to the Intensive Care Units (ICU) is a major global concern.
1 code implementation • 16 Oct 2020 • Kristoffer Wickstrøm, Karl Øyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen
A measure of uncertainty in the relevance scores is computed by taking the standard deviation across the relevance scores produced by each model in the ensemble, which in turn is used to make the explanations more reliable.
no code implementations • 27 Feb 2020 • Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Robert Jenssen
A large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important information about a patient's health status.
no code implementations • 10 Jul 2019 • Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen
To overcome this limitation, we present a kernel capable of exploiting the potentially rich information in the missing values and patterns, as well as the information from the observed data.
no code implementations • 20 Feb 2019 • Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi, Robert Jenssen
With the proposed Noisy multi-label semi-supervised dimensionality reduction (NMLSDR) method, the noisy multi-labels are denoised and unlabeled data are labeled simultaneously via a specially designed label propagation algorithm.
no code implementations • 9 May 2018 • Filippo Maria Bianchi, Lorenzo Livi, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen
In this work, we propose a novel autoencoder architecture based on recurrent neural networks to generate compressed representations of MTS.
no code implementations • 21 Mar 2018 • Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen
A large fraction of the electronic health records consists of clinical measurements collected over time, such as blood tests, which provide important information about the health status of a patient.
no code implementations • 17 Nov 2017 • Andreas Storvik Strauman, Filippo Maria Bianchi, Karl Øyvind Mikalsen, Michael Kampffmeyer, Cristina Soguero-Ruiz, Robert Jenssen
Clinical measurements that can be represented as time series constitute an important fraction of the electronic health records and are often both uncertain and incomplete.
1 code implementation • 20 Oct 2017 • Filippo Maria Bianchi, Karl Øyvind Mikalsen, Robert Jenssen
Clinical measurements collected over time are naturally represented as multivariate time series (MTS), which often contain missing data.
1 code implementation • 3 Apr 2017 • Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruiz, Robert Jenssen
An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel.