Diabetes

Plasma Proteomics and Diabetes Duration Predict Aneurysm Incidence and Rupture Using Machine Learning

Early identification of individuals at high risk for aneurysms, particularly ruptured aneurysms, is critical for timely intervention. However, existing imaging-free prediction models have significant limitations. This study aims to develop a robust model for predicting aneurysm incidence and rupture by leveraging multi-omics data, including circulating proteomics, and identifying specific biomarke...

Evaluating prediction of short-term tolerability of five type 2 diabetes drug classes using routine clinical features: UK population-based study

A precision medicine approach in type 2 diabetes (T2D) needs to consider potential treatment risks alongside established benefits for glycaemic and cardiometabolic outcomes. Considering five major T2D drug classes, we aimed to describe variation in short-term discontinuation (a proxy of overall tolerability) by drug and patient routine clinical features and determine whether combining features in ...

Predicting Depression in Canadians with or at Risk of Diabetes: A Cross-Sectional Machine Learning Analysis

Depression often goes unrecognized in individuals at risk or living with diabetes, presenting considerable challenges for primary care clinicians. Alt...

Large language model-assisted causal machine learning for identifying fatigue-related poor glycated hemoglobin in type 2 diabetes

Fatigue is common but mostly untreated in type 2 diabetes, since it requires a diagnostic workup which is hardly justified by fatigue alone. Individua...

Genetic Architecture and Risk Prediction of Gestational Diabetes Mellitus in over 116,144 Chinese Pregnancies

Gestational diabetes mellitus (GDM), a heritable metabolic disorder and the most common pregnancy-related condition, remains understudied regarding it...

In-context learning for data-efficient classification of diabetic retinopathy with multimodal foundation models

In-context learning, a prompt-based learning mechanism that enables multimodal foundation models to adapt to new tasks, can eliminate the need for ret...

A Combined Predictive and Causal Approach for Neighborhood-Level Diabetes Detection

Develop a neighborhood-level framework using machine learning and causal inference to identify socioeconomic and behavioral drivers of Type 2 diabetes...

Spine age estimation using deep learning in lateral spine radiographs and DXA VFA to predict incident fracture and mortality

Spine age estimated from lateral spine radiographs and DXA vertebral fracture assessments (VFAs) could be associated with fracture and mortality risk....

Real-World Type 2 Diabetes Second-Line Treatment Allocation Among Patients

This study aimed to evaluate the impact of socioeconomic disparities on the allocation of second-line treatments among patients with type 2 diabetes (...

Episode-Driven Insights: Can Large Language Models Tackle Multimodal Diabetes Data?

This study explores the potential of state-of-the-art large language models (LLM) to scaffold type 1 diabetes management by automating the analysis of...

GlucoseGo: A Simple, User-Friendly, Machine Learning-Derived Tool for Predicting Exercise-Related Hypoglycaemia Risk in Type 1 Diabetes

This study aims to develop an accessible, machine learning-derived tool for people with type 1 diabetes that predicts hypoglycaemia risk at the start ...

Evaluation of Machine Learning Models for Early Prediction of Gestational Diabetes Using Retrospective Electronic Health Records from Current and Previous Pregnancies

To assess the performance of machine learning (ML) models in predicting gestational diabetes mellitus (GDM) using electronic health record (EHR) data ...

Extracting Carotid Stenosis Severity from Clinical Notes Using Natural Language Processing: Development, Validation, and Application in a Nationwide Veteran Cohort

Carotid stenosis, which is atherosclerotic narrowing of the extracranial carotid arteries, is an important risk factor for ischemic stroke. The preval...

Developing a GraphRAG-enabled local-LLM for Gestational Diabetes Mellitus

This paper re-imagines a world of abundance in the treatment of chronic diseases such as Tpe 2 Diabetes. It asks: what if preventive and diagnostic re...

Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting

This study aimed to identify prognostic factors associated with poor outcomes of COVID-19 at diagnosis in Primary Health Care (PHC). We conducted a re...

Nocturnal Glycemic Stability Index

The Nocturnal Glycemic Stability Index (NGSI) is a novel quantitative metric designed to comprehensively assess overnight glucose stability by integra...

RFA-U-Net: A Foundation Model-Driven Approach for Accurate Choroid Segmentation in OCT Imaging

The choroid layer plays a critical role in maintaining outer retinal health and is implicated in numerous vision-threatening diseases such as diabetic...

Integrative Machine Learning Approach to Risk Prediction for Dementia and Alzheimer’s Disease

Dementia, especially Alzheimer’s disease (AD), is a major global health challenge marked by progressive cognitive impairment, behavioral changes, and ...

Prediction of Cardiovascular and Renal Complications of Diabetes by a multi-Polygenic Risk Score in Different Ethnic Groups

We developed a multi-Polygenic risk score (multiPRS) to predict the risk of nephropathy, stroke, and myocardial infarction in people with type 2 diabe...

Assessing Large Language Model Utility and Limitations in Diabetes Education: A Cross-Sectional Study of Patient Interactions and Specialist Evaluations

To assess the value of an AI-powered conversational agent in supporting diabetes self-management among adults with diabetic retinopathy and limited ed...