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Related Concept Videos

Diabetes Mellitus: Type 2 and Gestational01:22

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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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Diabetes Mellitus: Overview and Type I Subtype01:22

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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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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: Symptoms, Diagnosis, and Complications01:15

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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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Pathophysiology of Diabetes01:20

Pathophysiology of Diabetes

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Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
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Diabetes: Management and Pharmacotherapy01:15

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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.
Insulin remains the cornerstone of treatment for most patients with type 1 and many...
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A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
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Type 1 Diabetes Hypoglycemia Prediction Algorithms: Systematic Review.

Stella Tsichlaki1, Lefteris Koumakis2, Manolis Tsiknakis1,2

  • 1Department of Electrical & Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece.

JMIR Diabetes
|July 21, 2022
PubMed
Summary
This summary is machine-generated.

Predictive models using machine learning and deep learning show high accuracy in forecasting hypoglycemia for type 1 diabetes (T1D) patients. These advanced technologies enhance blood glucose monitoring and aid in preventing dangerous low blood sugar events.

Keywords:
artificial intelligencecontinuous glucose monitoringheart rate variabilityhypoglycemiapredictive modelstype 1 diabetes

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Area of Science:

  • Biomedical Engineering
  • Data Science in Healthcare
  • Endocrinology

Background:

  • Type 1 Diabetes (T1D) management requires continuous blood glucose monitoring.
  • Hypoglycemia is a significant risk associated with T1D, potentially leading to severe health complications.
  • Timely detection and prevention of hypoglycemia are crucial for patient well-being.

Purpose of the Study:

  • To review innovative detection techniques for hypoglycemia in T1D patients.
  • To identify and analyze predictive models for preventing hypoglycemic episodes.
  • To focus on technological advancements in T1D self-management.

Main Methods:

  • Systematic literature search using PRISMA guidelines.
  • Databases searched include PubMed, Google Scholar, IEEE Xplore, and ACM Digital Library.
  • Focus on technologies for hypoglycemia detection in T1D.

Main Results:

  • 19 predictive models for T1D hypoglycemia were identified.
  • Algorithmic approaches included statistics (10%), machine learning (52%), and deep learning (38%).
  • Models achieved satisfactory accuracies ranging from 70% to 99%.

Conclusions:

  • Continuous glucose monitoring improves diabetes control.
  • Predictive models using noninvasive sensors (wristbands, smartwatches) are the future of mobile health for T1D.
  • Further prospective studies are needed to validate these models in real-world mobile health interventions.