AIMC Topic: Diabetes Mellitus, Type 1
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GARNN: An interpretable graph attentive recurrent neural network for predicting blood glucose levels via multivariate time series.
Neural networks : the official journal of the International Neural Network Society
Feb 5, 2025
Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with type 1 or 2 diabetes, thereby reducing complications and improving quality of life. The state of the art of BG prediction has been ac...
Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling.
BMC medical informatics and decision making
Jan 31, 2025
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
Jan 27, 2025
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...
Predictors of glycaemic improvement in children and young adults with type 1 diabetes and very elevated HbA1c using the MiniMed 780G system.
Diabetes, obesity & metabolism
Jan 20, 2025
AIMS: This study aimed to identify key factors with the greatest influence on glycaemic outcomes in young individuals with type 1 diabetes (T1D) and very elevated glycaemia after 3 months of automated insulin delivery (AID).
Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning.
IEEE transactions on bio-medical engineering
Jan 15, 2025
OBJECTIVE: The artificial pancreas (AP) shows promise for closed-loop glucose control in type 1 diabetes mellitus (T1DM). However, designing effective control policies for the AP remains challenging due to complex physiological processes, delayed ins...
Machine learning techniques to predict diabetic ketoacidosis and HbA1c above 7% among individuals with type 1 diabetes - A large multi-centre study in Australia and New Zealand.
Nutrition, metabolism, and cardiovascular diseases : NMCD
Jan 9, 2025
BACKGROUND AND AIMS: Type 1 diabetes and diabetic ketoacidosis (DKA) have a significant impact on individuals and society across a wide spectrum. Our objective was to utilize machine learning techniques to predict DKA and HbA1c>7 %.
Middle Aged
Risk Assessment
Deep Learning
Adolescent
Machine Learning
Humans
Young Adult
Decision Support Techniques
Adult
Prospective Studies
Australia
Female
Diabetes Mellitus, Type 1
Male
Biomarkers
Glycated Hemoglobin
Predictive Value of Tests
Risk Factors
Diabetic Ketoacidosis
Prognosis
New Zealand
Support Vector Machine
Predicting and Ranking Diabetic Ketoacidosis Risk Among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.
Diabetes technology & therapeutics
Jan 6, 2025
To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-related hospitalization or emergency care in youth with type 1 diabetes (T1D). To achieve a sharable pr...
Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes.
Diabetologia
Dec 19, 2024
Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals...
A prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes.
BMC medical informatics and decision making
Dec 18, 2024
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 ...
Hybrid CNN-GRU Model for Real-Time Blood Glucose Forecasting: Enhancing IoT-Based Diabetes Management with AI.
Sensors (Basel, Switzerland)
Nov 30, 2024
For people with diabetes, controlling blood glucose level (BGL) is a significant issue since the disease affects how the body metabolizes food, which makes careful insulin regulation necessary. Patients have to manually check their blood sugar levels...
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