A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism.

Journal: Journal of medical Internet research
Published Date: Aug 12, 2020

Abstract

BACKGROUND: Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient data set for the other classes. Infection incidence often brings prolonged hyperglycemia and frequent insulin injections in people with type 1 diabetes, which are significant anomalies. Despite these potentials, there have been very few studies that focused on detecting infection incidences in individuals with type 1 diabetes using a dedicated personalized health model.

Authors

  • Ashenafi Zebene Woldaregay
    Department of Computer Science, University of Tromsø-The Arctic University of Norway, Tromsø, Norway. Electronic address: ashenafi.z.woldaregay@uit.no.
  • Ilkka Kalervo Launonen
    Department of Clinical Research, University Hospital of North Norway, Tromsø, Norway.
  • David Albers
    Department of Biomedical Informatics, Columbia University, N.Y., USA.
  • Jorge Igual
    Universitat Politècnica de València, Valencia, Spain.
  • Eirik Årsand
    Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway.
  • Gunnar Hartvigsen
    Norwegian Centre for Integrated Care and Telemedicine University Hospital of North Norway, Tromsø, Norway.

Keywords

External Resources

Popular Topics
Recent Journals