AIMC Topic: Diabetes Mellitus, Type 1
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Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning.
Frontiers in endocrinology
Mar 18, 2025
BACKGROUND: Diabetic cardiomyopathy (DC) is a serious complication in patients with type 1 diabetes mellitus and has become a growing public health problem worldwide. There is evidence that endoplasmic reticulum stress (ERS) is involved in the pathog...
Disease diagnostics using machine learning of B cell and T cell receptor sequences.
Science (New York, N.Y.)
Feb 21, 2025
Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system's own record of antigen exposures encoded by receptors on B cells and T ce...
Deep learning-based prediction of autoimmune diseases.
Scientific reports
Feb 7, 2025
Autoimmune Diseases are a complex group of diseases caused by the immune system mistakenly attacking body tissues. Their etiology involves multiple factors such as genetics, environmental factors, and abnormalities in immune cells, making prediction ...
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 %.
Male
Middle Aged
Risk Assessment
Deep Learning
Risk Factors
Young Adult
Adult
Australia
Predictive Value of Tests
Glycated Hemoglobin
New Zealand
Adolescent
Diabetes Mellitus, Type 1
Diabetic Ketoacidosis
Support Vector Machine
Humans
Machine Learning
Decision Support Techniques
Prospective Studies
Biomarkers
Prognosis
Female
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...
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