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Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice
L Zhang, L Yang, Z Zhou - Frontiers in Public Health, 2023 - frontiersin.org
Background and objective Hypoglycemia is a key barrier to achieving optimal glycemic
control in people with diabetes, which has been proven to cause a set of deleterious …
control in people with diabetes, which has been proven to cause a set of deleterious …
Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective
ABSTRACT Introduction Autoimmune disorders affect 4.5% to 9.4% of children, significantly
reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are …
reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are …
A machine learning model for week-ahead hypoglycemia prediction from continuous glucose monitoring data
F Giammarino, R Senanayake… - Journal of Diabetes …, 2024 - journals.sagepub.com
Background: Remote patient monitoring (RPM) programs augment type 1 diabetes (T1D)
care based on retrospective continuous glucose monitoring (CGM) data. Few methods are …
care based on retrospective continuous glucose monitoring (CGM) data. Few methods are …
A novel few shot learning derived architecture for long-term HbA1c prediction
Regular monitoring of glycated hemoglobin (HbA1c) levels is important for the proper
management of diabetes. Studies demonstrated that lower levels of HbA1c play an essential …
management of diabetes. Studies demonstrated that lower levels of HbA1c play an essential …
[HTML][HTML] Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review
Background Wearable technologies have become increasingly prominent in health care.
However, intricate machine learning and deep learning algorithms often lead to the …
However, intricate machine learning and deep learning algorithms often lead to the …
Long-term glucose forecasting for open-source automated insulin delivery systems: a machine learning study with real-world variability analysis
Glucose forecasting serves as a backbone for several healthcare applications, including real-
time insulin dosing in people with diabetes and physical activity optimization. This paper …
time insulin dosing in people with diabetes and physical activity optimization. This paper …
Blood glucose forecasting from temporal and static information in children with T1D
Background The overarching goal of blood glucose forecasting is to assist individuals with
type 1 diabetes (T1D) in avoiding hyper-or hypoglycemic conditions. While deep learning …
type 1 diabetes (T1D) in avoiding hyper-or hypoglycemic conditions. While deep learning …
A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graph
Pediatric diabetes I is an endemic and an especially difficult disease; indeed, at this point,
there does not exist a cure, but only careful management that relies on anticipating …
there does not exist a cure, but only careful management that relies on anticipating …
Riemannian manifold-based geometric clustering of continuous glucose monitoring to improve personalized diabetes management
Abstract Background: Continuous Glucose Monitoring (CGM) provides a detailed
representation of glucose fluctuations in individuals, offering a rich dataset for understanding …
representation of glucose fluctuations in individuals, offering a rich dataset for understanding …
Artificial Intelligence to Diagnose Complications of Diabetes
Artificial intelligence (AI) is increasingly being used to diagnose complications of diabetes.
Artificial intelligence is technology that enables computers and machines to simulate human …
Artificial intelligence is technology that enables computers and machines to simulate human …