AIMC Topic: Diabetes Mellitus, Type 2

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Predicting Type 2 diabetes onset age using machine learning: A case study in KSA.

PloS one
The rising prevalence of Type 2 Diabetes (T2D) in Saudi Arabia presents significant healthcare challenges. Estimating the age at onset of T2D can aid early interventions, potentially reducing complications due to late diagnoses. This study, conducted...

Association Between Aortic Imaging Features and Impaired Glucose Metabolism: A Deep Learning Population Phenotyping Approach.

Academic radiology
RATIONALE AND OBJECTIVES: Type 2 diabetes is a known risk factor for vascular disease with an impact on the aorta. The aim of this study was to develop a deep learning framework for quantification of aortic phenotypes from magnetic resonance imaging ...

Development and Validation of a Machine Learning Algorithm for Predicting Diabetes Retinopathy in Patients With Type 2 Diabetes: Algorithm Development Study.

JMIR medical informatics
BACKGROUND: Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. Machine learning (ML) systems can enhance DR in community-based screening. However, predictive power models for usability and performance are still being d...

Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes.

Journal of health, population, and nutrition
BACKGROUND: Diabetes mellitus, an endocrine system disease, is a common disease involving many patients worldwide. Many studies are performed to evaluate the correlation between micronutrients/macronutrients on diabetes but few of them have a high st...

Predicting cardiovascular outcomes in Chinese patients with type 2 diabetes by combining risk factor trajectories and machine learning algorithm: a cohort study.

Cardiovascular diabetology
BACKGROUND: Cardiovascular complications are major concerns for Chinese patients with type 2 diabetes. Accurately predicting these risks remains challenging due to limitations in traditional risk models. We aimed to develop a dynamic prediction model...

Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI).

BMJ open
INTRODUCTION: Artificial Intelligence Ready and Equitable for Diabetes Insights (AI-READI) is a data collection project on type 2 diabetes mellitus (T2DM) to facilitate the widespread use of artificial intelligence and machine learning (AI/ML) approa...

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
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...

REMED-T2D: A robust ensemble learning model for early detection of type 2 diabetes using healthcare dataset.

Computers in biology and medicine
Early diagnosis and timely treatment of diabetes are critical for effective disease management and the prevention of complications. Undiagnosed diabetes can lead to an increased risk of several health issues. Although numerous machine learning (ML) m...

A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.

Nature communications
Cells are regulated at multiple levels, from regulations of individual genes to interactions across multiple genes. Some recent neural network models can connect molecular changes to cellular phenotypes, but their design lacks modeling of regulatory ...

PPARγ modulator predictor (PGMP_v1): chemical space exploration and computational insights for enhanced type 2 diabetes mellitus management.

Molecular diversity
Peroxisome proliferator-activated receptor gamma (PPARγ) plays a critical role in adipocyte differentiation and enhances insulin sensitivity. In contemporary drug discovery, in silico design strategies offer significant advantages by revealing essent...