AIMC Topic: Diabetes Mellitus, Type 2
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Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme.
The British journal of ophthalmology
May 21, 2024
BACKGROUND/AIMS: National guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on images to predict progression to ref...
Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery.
Nutrients
May 20, 2024
BACKGROUND: This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques.
Longitudinal artificial intelligence-based deep learning models for diagnosis and prediction of the future occurrence of polyneuropathy in diabetes and prediabetes.
Neurophysiologie clinique = Clinical neurophysiology
May 18, 2024
OBJECTIVE: The objective of this study was to develop artificial intelligence-based deep learning models and assess their potential utility and accuracy in diagnosing and predicting the future occurrence of diabetic distal sensorimotor polyneuropathy...
AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.
Nature communications
May 18, 2024
Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particu...
Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial.
Trials
May 16, 2024
BACKGROUND: Prediabetes is a highly prevalent condition that heralds an increased risk of progression to type 2 diabetes, along with associated microvascular and macrovascular complications. The Diabetes Prevention Program (DPP) is an established eff...
Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence.
International journal of molecular sciences
May 14, 2024
Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study aimed to leverage machine learning (ML) algorithms to predict the 4-year risk of developing type 2 dia...
Pre-hospital glycemia as a biomarker for in-hospital all-cause mortality in diabetic patients - a pilot study.
Cardiovascular diabetology
May 3, 2024
BACKGROUND: Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with considerable economic ramifications. While blood glucose management and long-term metabolic target setting for home care and outpatient treatment follow est...
A Scoping Review of Studies Using Artificial Intelligence Identifying Optimal Practice Patterns for Inpatients With Type 2 Diabetes That Lead to Positive Healthcare Outcomes.
Computers, informatics, nursing : CIN
May 1, 2024
The objective of this scoping review was to survey the literature on the use of AI/ML applications in analyzing inpatient EHR data to identify bundles of care (groupings of interventions). If evidence suggested AI/ML models could determine bundles, t...
Identifying Main Themes in Diabetes Management Interviews Using Natural Language Processing-Based Text Mining.
Computers, informatics, nursing : CIN
May 1, 2024
This study aimed to identify the main themes from exit interviews of adult patients with type 2 diabetes after completion of a diabetes education program. Eighteen participants with type 2 diabetes completed an exit interview regarding their program ...
Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning.
Diabetes & metabolism journal
Apr 30, 2024
BACKGRUOUND: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receive...
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