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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: 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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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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Pathophysiology of Diabetes01:20

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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: 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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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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Updated: Aug 10, 2025

Improving IV Insulin Administration in a Community Hospital
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Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.

Liyin Zhang1, Lin Yang1, Zhiguang Zhou1

  • 1National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China.

Frontiers in Public Health
|February 13, 2023
PubMed
Summary

Hypoglycemia prediction using machine learning models is crucial for diabetes management. This review summarizes current algorithms and risk factors to improve clinical prevention strategies and patient outcomes.

Keywords:
data-based algorithms or modelsdiabetes mellitushypoglycemiamachine learningprediction

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Area of Science:

  • Endocrinology and Metabolism
  • Biomedical Data Science
  • Artificial Intelligence in Healthcare

Background:

  • Hypoglycemia poses a significant challenge to optimal glycemic control in diabetes, leading to adverse outcomes like cognitive impairment, cardiovascular disease, and mortality.
  • Effective hypoglycemia prediction is increasingly vital for diabetes management, driven by advancements in big data analysis and machine learning (ML).
  • A comprehensive review of existing prediction algorithms and models is necessary to guide clinical practice and enhance hypoglycemia prevention strategies.

Approach:

  • A systematic literature search was conducted across PubMed, EMBASE, and the Cochrane Library for studies published between January 1, 2015, and December 8, 2022.
  • The review encompassed five key aspects of hypoglycemia prediction: real-time, mild and severe, nocturnal, inpatient, and other hypoglycemia types (postprandial, exercise-related).
  • A total of 79 studies were included from 5,042 retrieved records for analysis.

Key Points:

  • Two primary categories of prediction models were identified: traditional logistic regression models using clinical data and data-driven ML models, with continuous glucose monitoring data being most prevalent.
  • Clinical data-based models have identified various risk factors contributing to hypoglycemic events.
  • ML models employing techniques like neural networks, autoregressive methods, ensemble learning, and supervised learning have revealed significant predictive features for hypoglycemia.

Conclusions:

  • This study provides an in-depth analysis of established hypoglycemia prediction models and identified risk factors from multiple viewpoints.
  • The findings offer insights into current trends and future directions in hypoglycemia prediction research.
  • Understanding these models and risk factors can lead to improved clinical decision-making and proactive hypoglycemia prevention in diabetes care.