Search Results for author: Karl Øyvind Mikalsen

Found 10 papers, 3 papers with code

Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series

1 code implementation16 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.

Time Series

A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs

no code implementations27 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.

Ensemble Learning Imputation +1

Time series cluster kernels to exploit informative missingness and incomplete label information

no code implementations10 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.

Ensemble Learning Imputation +1

Noisy multi-label semi-supervised dimensionality reduction

no code implementations20 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.

Supervised dimensionality reduction

An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples

no code implementations21 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.

General Classification Imputation +1

Learning compressed representations of blood samples time series with missing data

1 code implementation20 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.

General Classification Time Series

Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data

1 code implementation3 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.

Ensemble Learning Time Series +1

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