Modeling the fasting blood glucose response to basal insulin adjustment in type 2 diabetes: An explainable machine learning approach on real-world data.

Journal: International journal of medical informatics
PMID: 39705917

Abstract

INTRODUCTION: Optimal basal insulin titration for people with type 2 diabetes is vital to effectively reducing the risk of complications. However, a sizeable proportion of people (30-50 %) remain in suboptimal glycemic control six months post-initiation of basal insulin. This indicates a clear need for novel titration methods that account for individual patient variability in real-world settings.

Authors

  • Camilla Heisel Nyholm Thomsen
    Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
  • Thomas Kronborg
    Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
  • Stine Hangaard
    Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
  • Peter Vestergaard
    Department of Endocrinology, Aalborg University Hospital, Moelleparkvej 4, 9000, Aalborg, Denmark.
  • Ole Hejlesen
    Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
  • Morten Hasselstrøm Jensen
    Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.

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