AIMC Topic: Diabetes Mellitus, Type 1
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Machine learning approach reveals microbiome, metabolome, and lipidome profiles in type 1 diabetes.
Journal of advanced research
Nov 30, 2023
INTRODUCTION: Type 1 diabetes (T1D) is a complex disorder influenced by genetic and environmental factors. The gut microbiome, the serum metabolome, and the serum lipidome have been identified as key environmental factors contributing to the pathophy...
Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges.
Diabetologia
Nov 18, 2023
The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasi...
Electroencephalographic abnormalities in children with type 1 diabetes mellitus: a prospective study.
Turkish journal of medical sciences
Oct 12, 2023
BACKGROUND/AIM: The aim herein was to investigate epileptiform discharges on electroencephalogram (EEG), their correlation with glutamic acid decarboxylase 65 autoantibody (GAD-ab) in newly diagnosed pediatric type 1 diabetes mellitus (T1DM) patients...
Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes? A prospective cohort study from the national screening programme in Scotland.
International journal of medical informatics
Apr 18, 2023
AIMS: This study's objective was to evaluate whether deep learning (DL) on retinal photographs from a diabetic retinopathy screening programme improve prediction of incident cardiovascular disease (CVD).
Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study.
Journal of diabetes science and technology
Jan 11, 2023
BACKGROUND: The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitor...
Personalized Blood Glucose Prediction for Type 1 Diabetes Using Evidential Deep Learning and Meta-Learning.
IEEE transactions on bio-medical engineering
Dec 26, 2022
The availability of large amounts of data from continuous glucose monitoring (CGM), together with the latest advances in deep learning techniques, have opened the door to a new paradigm of algorithm design for personalized blood glucose (BG) predicti...
Temporal deep learning framework for retinopathy prediction in patients with type 1 diabetes.
Artificial intelligence in medicine
Sep 26, 2022
The adoption of electronic health records in hospitals has ensured the availability of large datasets that can be used to predict medical complications. The trajectories of patients in real-world settings are highly variable, making longitudinal data...
Deep learning fuzzy immersion and invariance control for type-I diabetes.
Computers in biology and medicine
Aug 17, 2022
In this study, a novel approach is proposed for glucose regulation in type-I diabetes patients. Unlike most studies, the glucose-insulin metabolism is considered to be uncertain. A new approach on the basis of the Immersion and Invariance (I&I) theor...
Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models.
Sensors (Basel, Switzerland)
Jun 30, 2022
In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with t...
Predicting age at onset of type 1 diabetes in children using regression, artificial neural network and Random Forest: A case study in Saudi Arabia.
PloS one
Feb 28, 2022
The rising incidence of type 1 diabetes (T1D) among children is an increasing concern globally. A reliable estimate of the age at onset of T1D in children would facilitate intervention plans for medical practitioners to reduce the problems with delay...
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