AIMC Topic: Diabetes Mellitus, Type 2
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Immunometabolic alterations in type 2 diabetes mellitus revealed by single-cell RNA sequencing: insights into subtypes and therapeutic targets.
Frontiers in immunology
Jan 14, 2025
BACKGROUND: Type 2 Diabetes Mellitus (T2DM) represents a major global health challenge, marked by chronic hyperglycemia, insulin resistance, and immune system dysfunction. Immune cells, including T cells and monocytes, play a pivotal role in driving ...
Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis.
BMC medical informatics and decision making
Jan 13, 2025
BACKGROUND: This systematic review aims to explore the early predictive value of machine learning (ML) models for the progression of gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM).
AntiT2DMP-Pred: Leveraging feature fusion and optimization for superior machine learning prediction of type 2 diabetes mellitus.
Methods (San Diego, Calif.)
Jan 10, 2025
Pancreatic α-amylase breaks down starch into isomaltose and maltose, which are further hydrolyzed by α-glucosidase in the intestine into monosaccharides, rapidly raising blood sugar levels and contributing to type 2 diabetes mellitus (T2DM). Syntheti...
Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?
BMC endocrine disorders
Jan 10, 2025
BACKGROUND: The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle - income countries call for preventive public health interventions. Studies from Africa including those from Ghana, consistently reveal high T2DM-related mor...
Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes.
Cardiovascular diabetology
Jan 2, 2025
BACKGROUND: Prior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learnin...
Predictive Value of Tests
Heart Disease Risk Factors
Image Interpretation, Computer-Assisted
Photography
Deep Learning
Female
Time Factors
Reproducibility of Results
Diabetes Mellitus, Type 2
Risk Factors
Humans
Male
Aged
Diabetic Retinopathy
Cardiovascular Diseases
Decision Support Techniques
Middle Aged
Retinal Vessels
Prognosis
Risk Assessment
Type 2 diabetes prediction method based on dual-teacher knowledge distillation and feature enhancement.
Scientific reports
Jan 2, 2025
Diabetes prediction is an important topic in the field of medical health. Accurate prediction can help early intervention and reduce patients' health risks and medical costs. This paper proposes a data preprocessing method, including removing outlier...
Developing an AI-Based clinical decision support system for basal insulin titration in type 2 diabetes in primary Care: A Mixed-Methods evaluation using heuristic Analysis, user Feedback, and eye tracking.
International journal of medical informatics
Dec 31, 2024
BACKGROUND AND AIM: The progressive nature of type 2 diabetes often, in time, necessitates basal insulin therapy to achieve glycemic targets. However, despite standardized titration algorithms, many people remain poorly controlled after initiating in...
In silico design of dehydrophenylalanine containing peptide activators of glucokinase using pharmacophore modelling, molecular dynamics and machine learning: implications in type 2 diabetes.
Journal of computer-aided molecular design
Dec 31, 2024
Diabetes represents a significant global health challenge associated with substantial healthcare costs and therapeutic complexities. Current diabetes therapies often entail adverse effects, necessitating the exploration of novel agents. Glucokinase (...
Heterogeneous blood pressure treatment effects on cognitive decline in type 2 diabetes: A machine learning analysis of a randomized clinical trial.
Diabetes, obesity & metabolism
Dec 26, 2024
AIM: We aimed to identify the characteristics of patients with diabetes who can derive cognitive benefits from intensive blood pressure (BP) treatment using machine learning methods.
Mixed-effects neural network modelling to predict longitudinal trends in fasting plasma glucose.
BMC medical research methodology
Dec 20, 2024
BACKGROUND: Accurate fasting plasma glucose (FPG) trend prediction is important for management and treatment of patients with type 2 diabetes mellitus (T2DM), a globally prevalent chronic disease. (Generalised) linear mixed-effects (LME) models and m...
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