Browsing Fakultet for teknologi, kunst og design (TKD) by Journals "Diagnostics (Basel)"

Now showing items 1-4 of 4

    • A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering 

      Thunold, Håvard Horgen; Riegler, Michael; Yazidi, Anis; Hammer, Hugo Lewi (Peer reviewed; Journal article, 2023)
      An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient ...

    • Identifying Autism Gaze Patterns in Five-Second Data Records 

      Lencastre, Pedro; Lotfigolian, Maryam; Lind, Pedro (Peer reviewed; Journal article, 2024)
      One of the most challenging problems when diagnosing autism spectrum disorder (ASD) is the need for long sets of data. Collecting data during such long periods is challenging, particularly when dealing with children. This ...

    • Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique 

      Iftikhar, Hasnain; Khan, Murad; Khan, Mohammed Saad; Khan, Mehak (Peer reviewed; Journal article, 2023)
      : In the modern world, new technologies such as artificial intelligence, machine learning, and big data are essential to support healthcare surveillance systems, especially for monitoring confirmed cases of monkeypox. The ...

    • Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis 

      Storås, Andrea; Andersen, Ole Emil; Lockhart, Sam; Thielemann, Roman; Gnesin, Filip; Thambawita, Vajira L B; Hicks, Steven; Kanters, Jørgen K.; Strumke, Inga; Halvorsen, Pål; Riegler, Michael (Peer reviewed; Journal article, 2023)
      Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide ...