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Related Concept Videos

Hypoglycemia and Glucagon01:15

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Without prolonged fasting, healthy individuals maintain blood glucose levels above 3.5 mM due to a well-adapted neuroendocrine counterregulatory system that effectively prevents acute hypoglycemia, a potentially life-threatening condition. The primary clinical scenarios for hypoglycemia encompass diabetes treatment, inappropriate production of endogenous insulin or insulin-like substances by tumors, and the use of glucose-lowering agents in non-diabetic individuals. Notably, hypoglycemia in the...
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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
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The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges.

Omer Mujahid1, Ivan Contreras1, Josep Vehi1,2

  • 1Model Identification and Control Laboratory, Institut d'Informatica i Applicacions, Universitat de Girona, 17003 Girona, Spain.

Sensors (Basel, Switzerland)
|January 20, 2021
PubMed
Summary

Machine learning aids in predicting hypoglycemia, a dangerous drop in blood glucose for diabetic patients. This review explores current trends and challenges in using AI for proactive hypoglycemia management.

Keywords:
artificial intelligencedecision support system (DSS)detectionhypoglycemiamachine learningprediction

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Healthcare
  • Diabetes Management

Background:

  • Hypoglycemia, a critical drop in blood glucose for diabetic patients, can lead to severe health consequences, including death.
  • A significant portion of severe hypoglycemia cases occur unexpectedly and without warning symptoms.
  • Proactive prediction of hypoglycemia is crucial for improving the quality of life for individuals with diabetes.

Purpose of the Study:

  • To review existing research on machine learning applications for hypoglycemia prediction.
  • To identify and discuss emerging trends in the field.
  • To highlight the challenges faced by researchers in hypoglycemia prediction using machine learning.

Main Methods:

  • A systematic literature review was conducted using PubMed and Google Scholar.
  • Manuscripts published within the last five years were prioritized.
  • An initial pool of 903 papers was narrowed down to 57 for detailed analysis.

Main Results:

  • Research was categorized into hypoglycemia prediction and hypoglycemia detection.
  • An overview of machine learning methodologies applied to hypoglycemia forecasting was provided.
  • Details on training data types and prediction horizons were analyzed across reviewed studies.

Conclusions:

  • Machine learning offers significant potential for anticipating hypoglycemic events in diabetic patients.
  • Further research is needed to address current challenges and refine prediction models.
  • Advancements in this area can lead to improved patient outcomes and proactive diabetes care.