Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling.

Journal: BMC medical informatics and decision making
PMID: 39891137

Abstract

BACKGROUND: Type 1 diabetes (T1D) is a chronic endocrine disorder characterized by high blood glucose levels, impacting millions of people globally. Its management requires intensive insulin therapy, frequent blood glucose monitoring, and lifestyle adjustments. The accurate prediction of the short-term course of glucose levels in the subcutaneous space in T1D people, as measured by a continuous glucose monitoring (CGM) system, is essential for improving glucose control by avoiding harmful hypoglycaemic and hyperglycaemic glucose swings, facilitating precise insulin management and individualized care and, in turn, minimizing long-term vascular complications.

Authors

  • Daphne N Katsarou
  • Eleni I Georga
  • Maria A Christou
    Department of Endocrinology, University Hospital of Ioannina, Ioannina, GR45110, Greece.
  • Panagiota A Christou
    Department of Endocrinology, University Hospital of Ioannina, Ioannina, GR45110, Greece.
  • Stelios Tigas
    Department of Endocrinology, University Hospital of Ioannina, Ioannina, GR45110, Greece.
  • Costas Papaloukas
    Department of Biological Applications and Technology, University of Ioannina, Ioannina, Greece.
  • Dimitrios I Fotiadis
    Biomedical Research Institute, Foundation for Research and Technology Hellas, Greece; Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Greece.

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