• Density ridge manifold traversal 

      Myhre, Jonas Nordhaug; Kampffmeyer, Michael C.; Jenssen, Robert (Chapter; Bokkapittel, 2017-06-19)
      The density ridge framework for estimating principal curves and surfaces has in a number of recent works been shown to capture manifold structure in data in an intuitive and effective manner. However, to date there exists no efficient way to traverse these manifolds as defined by density ridges. This is unfortunate, as manifold traversal is an important problem for example for shape estimation in ...
    • Learning similarities between irregularly sampled short multivariate time series from EHRs 

      Mikalsen, Karl Øyvind; Bianchi, Filippo Maria; Soguero-Ruiz, Cristina; Skrøvseth, Stein Olav; Lindsetmo, Rolv-Ole; Revhaug, Arthur; Jenssen, Robert (Conference object; Konferansebidrag, 2016-12-04)
      A large fraction of the Electronic Health Records consists of clinical multivariate time series. Building models for extracting information from these is important for improving the understanding of diseases, patient care and treatment. Such time series are oftentimes particularly challenging since they are characterized by multiple, possibly dependent variables, length variability and irregular ...
    • Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data 

      Kocbek, Primoz; Fijacko, Nino; Soguero-Ruiz, Cristina; Mikalsen, Karl Øyvind; Maver, Uros; Brzan, Petra Povalej; Stozer, Andraz; Jenssen, Robert; Skrøvseth, Stein Olav; Stiglic, Gregor (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-02-19)
      This study describes a novel approach to solve the surgical site infection (SSI) classification problem. Feature engineering has traditionally been one of the most important steps in solving complex classification problems, especially in cases with temporal data. The described novel approach is based on abstraction of temporal data recorded in three temporal windows. Maximum likelihood L1-norm ...
    • Reservoir computing approaches for representation and classification of multivariate time series 

      Bianchi, Filippo Maria; Scardapane, Simone; Løkse, Sigurd; Jenssen, Robert (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-06-29)
      Classification of multivariate time series (MTS) has been tackled with a large variety of methodologies and applied to a wide range of scenarios. Reservoir computing (RC) provides efficient tools to generate a vectorial, fixed-size representation of the MTS that can be further processed by standard classifiers. Despite their unrivaled training speed, MTS classifiers based on a standard RC ...
    • Robust clustering using a kNN mode seeking ensemble 

      Myhre, Jonas Nordhaug; Mikalsen, Karl Øyvind; Løkse, Sigurd; Jenssen, Robert (Journal article; Tidsskriftartikkel; Peer reviewed, 2017-12-02)
      In this paper we present a new algorithm for parameter-free clustering by mode seeking. Mode seeking, especially in the form of the mean shift algorithm, is a widely used strategy for clustering data, but at the same time prone to poor performance if the parameters are not chosen correctly. We propose to form a <i>clustering ensemble</i> consisting of repeated and bootstrapped runs of the recent kNN ...
    • Time series cluster kernel for learning similarities between multivariate time series with missing data 

      Mikalsen, Karl Øyvind; Bianchi, Filippo Maria; Soguero-Ruiz, Cristina; Jenssen, Robert (Journal article; Tidsskriftartikkel; Peer reviewed, 2017-12-06)
      <p>Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the ...
    • Using anchors from free text in electronic health records to diagnose postoperative delirium 

      Mikalsen, Karl Øyvind; Soguero-Ruiz, Cristina; Jensen, Kasper; Hindberg, Kristian; Gran, Mads; Revhaug, Arthur; Lindsetmo, Rolv-Ole; Skrøvseth, Stein Olav; Godtliebsen, Fred; Jenssen, Robert (Journal article; Tidsskriftartikkel; Peer reviewed, 2017-09-19)
      Objectives:<br> Postoperative delirium is a common complication after major surgery among the elderly. Despite its potentially serious consequences, the complication often goes undetected and undiagnosed. In order to provide diagnosis support one could potentially exploit the information hidden in free text documents from electronic health records usin ...