• Clinical Synthetic Data Generation to Predict and Identify Risk Factors for Cardiovascular Diseases 

      García-Vicente, Clara; Chushig-Muzo, David; Mora-Jimenez, Inmaculada; Fabelo, Himar; Gram, Inger Torhild; Løchen, Maja-Lisa; Granja, Conceição; Soguero-Ruiz, Cristina (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-01-21)
      Noncommunicable diseases are among the most significant health threats in our society, being cardiovascular diseases (CVD) the most prevalent. Because of the severity and prevalence of these illnesses, early detection and prevention are critical for reducing the worldwide health and economic burden. Though machine learning (ML) methods usually outperform conventional approaches in many domains, class ...
    • Design and Prestudy Assessment of a Dashboard for Presenting Self-Collected Health Data of Patients With Diabetes to Clinicians: Iterative Approach and Qualitative Case Study 

      Giordanengo, Alain; Årsand, Eirik; Woldaregay, Ashenafi Zebene; Bradway, Meghan; Grøttland, Astrid; Hartvigsen, Gunnar; Granja, Conceição; Torsvik, Torbjørn; Hansen, Anne Helen (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-07-09)
      <p><i>Background - </i>Introducing self-collected health data from patients with diabetes into consultation can be beneficial for both patients and clinicians. Such an initiative can allow patients to be more proactive in their disease management and clinicians to provide more tailored medical services. Optimally, electronic health record systems (EHRs) should be able to receive self-collected health ...
    • Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factors 

      García-Vicente, Clara; Chushig-Muzo, David; Mora-Jiménez, Inmaculada; Fabelo, Himar; Gram, Inger Torhild; Løchen, Maja-Lisa; Granja, Conceição; Soguero-Ruiz, Cristina (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-03-23)
      Machine Learning (ML) methods have become important for enhancing the performance of decision-support predictive models. However, class imbalance is one of the main challenges for developing ML models, because it may bias the learning process and the model generalization ability. In this paper, we consider oversampling methods for generating synthetic categorical clinical data aiming to improve the ...
    • Factors Determining the Success and Failure of eHealth Interventions: Systematic Review of the Literature 

      Granja, Conceição; Janssen, Wouter; Johansen, Monika Alise (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-05-01)
      <p><i>Background</i>: eHealth has an enormous potential to improve healthcare cost, effectiveness, and quality of care. However, there seems to be a gap between the foreseen benefits of research and clinical reality.</p> <p><i>Objective</i>: Our objective was to systematically review the factors influencing the outcome of eHealth interventions in terms of success and failure.</p> <p><i>Methods</i>: ...