Diabetes

Correcting heterogeneous diagnostic bias when developing clinical prediction models using causal hidden Markov models

In routine care, individuals identified a priori as high-risk are usually tested for conditions more frequently. Protected attributes, such as sex or ethnicity may also determine testing frequency. Such heterogeneous detection rates across a population induce label error. This causes systematic model error for specific groups and biases performance metrics during validation. This paper proposes ...

Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation

Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults worldwide, yet most automated screening systems are limited to image-level classification and lack clinically structured reporting. We propose Retina-RAG, a low-cost modular framework that jointly performs DR severity grading, macular edema (ME) detection, and report generation. The architecture decouple...

Artificial Intelligence Driven Support and Self Care Competence as Determinants of Medication Adherence in Diabetes Care, A Cross-sectional Nigerian Study

Medication adherence among patients with diabetes remains suboptimal in low and middle income countries, including Nigeria. Emerging digital health in...

Conserved neuroectodermal aging encodes primate health and longevity

Neuroectoderm-derived tissues are highly metabolically active and exhibit minimal regenerative turnover, rendering them uniquely vulnerable to age-rel...

Combinatorial epigenomic patterns define regulatory programs underlying disease heterogeneity

Disease is a heterogeneous process that involves multiple organs and cell types. Understanding how genomic variation contributes to disease requires a...

OphMAE: Bridging Volumetric and Planar Imaging with a Foundation Model for Adaptive Ophthalmological Diagnosis

The advent of foundation models has heralded a new era in medical artificial intelligence (AI), enabling the extraction of generalizable representatio...

A Digital Twin for Tracking and Forecasting Glycemia with Septic Patients in ICUs

We present a digital twin framework for real time glucose monitoring and forecasting in septic patients in intensive care units (ICUs). The framework ...

Dual GLP-1/FGF21 agonism suppresses voluntary alcohol consumption, alcohol choice, and nucleus accumbens dopamine modulation

Excessive alcohol consumption remains a major public health challenge with limited therapeutic options. Both glucagon-like peptide-1 (GLP-1) and fibro...

From Prediction to Practice: A Task-Aware Evaluation Framework for Blood Glucose Forecasting

Clinical time-series forecasting is increasingly studied for decision support, yet standard aggregate metrics can obscure whether a model is actually ...

Prediction of Alzheimer's Disease Risk Factors from Retinal Images via Deep Learning: Development and Validation of Biologically Relevant Morphological Associations in the UK Biobank

The systemic, metabolic, lifestyle factors have established associations with Alzheimer's Disease (AD) through epidemiologic and AD-specific biomarker...

Disease Risk Prediction Using Structured EHR Data: Can Generalist Large Language Models Match Specialized Clinical Foundation Models? A Comparative Evaluation with Fine-Tuning

Background: Electronic health records (EHRs) with clinical decision support tools are now ubiquitous in healthcare organizations. Clinical foundation ...

One Size Fits All? Comparing Foundation and Task-specific Models for Retinal Fluid Segmentation

Retinal fluids, detectable through optical coherence tomography (OCT), are key biomarkers for retinal diseases such as diabetic macular edema and age-...

Development and validation of a lesion-supervised deep learning system for diabetic retinopathy grading according to UK national screening criteria

Background: Diabetic retinopathy (DR) is the leading cause of preventable blindness among working-age adults worldwide, yet screening coverage remains...

Development of Explainable Machine Learning Framework for Early Detection and Risk Stratification of Diabetes in Age Specific Variations

Objective To develop and evaluate a novel machine learning (ML) framework tailored to a clinical diabetes dataset and to assess whether demographic st...

An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness

Artificial Intelligence and Machine Learning (AI/ML) models used in clinical settings are increasingly deployed to support clinical decision-making. H...

Impact of Age Specialized Models for Hypoglycemia Classification

Disease progression varies with age and is influenced by underlying genetic, biochemical, and hormonal etiologies, suggesting the need for tailored mo...

Semaglutide is associated with improved breast cancer survival, lower metastatic burden, and a dose-survival relationship uncoupled from weight-loss magnitude

Metabolic dysfunction is increasingly recognized as a risk factor for poor outcomes in breast cancer, but whether incretin-based therapies confer surv...

Multimodal prediction of visual improvement in diabetic macular edema using real-world electronic health records and optical coherence tomography images

Multimodal learning has the potential to improve clinical prediction by integrating complementary data sources, but the incremental value of imaging b...

Liver Biomarker Improves AHA/ACC 10-year ASCVD Risk Prediction in US and China Cohorts with ML

Introduction: Accurate stratification of hard atherosclerotic cardiovascular disease (ASCVD) risk remains challenging despite advances in prevention. ...

Robust Diabetic Retinopathy Grading Using Dual-Resolution Attention-Based Deep Learning with Ordinal Regression

Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, and automated grading systems play a crucial role in large-scale screenin...