AIMC Topic: Hypoglycemia

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Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection.

Scientific reports
In general, deficient birth weight neonates suffer from hypoglycemia, and this can be quite disadvantageous. Like oxygen, glucose is a building block of life and constitutes the significant share of energy produced by the fetus and the neonate during...

Personalized machine learning models for noninvasive hypoglycemia detection in people with type 1 diabetes using a smartwatch: Insights into feature importance during waking and sleeping times.

PloS one
Hypoglycemia is a major challenge for people with diabetes. Therefore, glycemic monitoring is an important aspect of diabetes management. However, current methods such as finger pricking and continuous glucose monitoring systems (CGMS) are invasive, ...

Computational modelling for risk assessment of neurological disorder in diabetes using Hodgkin-Huxley model.

Computer methods and programs in biomedicine
BACKGROUND: Diabetes mellitus, characterized by chronic glucose dysregulation, significantly increases the risk of neurological disorders such as cognitive decline, seizures, and Alzheimer's disease. As neurons depend on glucose for energy, fluctuati...

A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease.

Scientific reports
Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abnormal glycogen storage and breakdown. These disorders are caused by mutations in G6PC1, which is essential for proper glucose storage and metabolism. ...

Post-Bariatric Hypoglycemia After Gastric Bypass: Clinical Characteristics, Risk Factors, and Future Directions-A Response to Grover et al.

Clinical endocrinology
BACKGROUND: Post-bariatric hypoglycemia (PBH) after Roux-en-Y gastric bypass (RYGB) is a complex complication, often characterized by potentially severe hypoglycemic episodes and reduced hypoglycemia awareness. Recent findings suggest that autonomic ...

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

BMC medical informatics and decision making
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 a...

A safe-enhanced fully closed-loop artificial pancreas controller based on deep reinforcement learning.

PloS one
Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enabl...

Towards a decision support system for post bariatric hypoglycaemia: development of forecasting algorithms in unrestricted daily-life conditions.

BMC medical informatics and decision making
BACKGROUND: Post bariatric hypoglycaemic (PBH) is a late complication of weight loss surgery, characterised by critically low blood glucose levels following meal-induced glycaemic excursions. The disabling consequences of PBH underline the need for t...

A prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes.

BMC medical informatics and decision making
Nocturnal hypoglycemia is a critical problem faced by diabetic patients. Failure to intervene in time can be dangerous for patients. The existing early warning methods struggle to extract crucial information comprehensively from complex multi-source ...

Improving Clinical Preparedness: Community Health Nurses and Early Hypoglycemia Prediction in Type 2 Diabetes Using Hybrid Machine Learning Techniques.

Public health nursing (Boston, Mass.)
OBJECTIVES: The aim of the study was to analyze the data of diabetic patients regarding warning signs of hypoglycemia to predict it at an early stage using various novel machine learning (ML) algorithms. Individual interviews with diabetic patients w...