Diabetes

Integrating Bioinformatics and Machine Learning to Identify Glucose Metabolism-Related Biomarkers with Diagnostic and Prognostic Value for Patients with Kidney Renal Clear Cell Carcinoma.

BACKGROUND: Glucose metabolism plays a critical role in the development and progression of kidney renal clear cell carcinoma (KIRC). This study aimed to identify glucose metabolism-related biomarkers (GRBs) and therapeutic targets for KIRC diagnosis and prognosis using bioinformatics and machine learning.

Integrating Large Language Models with Human Expertise for Disease Detection in Electronic Health Records

Objective: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EHR is labour-intensive and requires extensive manual labelling of disease outcomes. This study developed an efficient strategy based on advanced large language models to identify multiple conditions from EHR ...

What is the role of human decisions in a world of artificial intelligence: an economic evaluation of human-AI collaboration in diabetic retinopathy screening

As Artificial intelligence (AI) has been increasingly integrated into the medical field, the role of humans may become vague. While numerous studies...

Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection

Multi-view diabetic retinopathy (DR) detection has recently emerged as a promising method to address the issue of incomplete lesions faced by single...

Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling

Type 1 Diabetes (T1D) management is a complex task due to many variability factors. Artificial Pancreas (AP) systems have alleviated patient burden ...

The Role of Artificial Intelligence in Enhancing Insulin Recommendations and Therapy Outcomes

The growing worldwide incidence of diabetes requires more effective approaches for managing blood glucose levels. Insulin delivery systems have adva...

Limits of trust in medical AI

Artificial intelligence (AI) is expected to revolutionize the practice of medicine. Recent advancements in the field of deep learning have demonstra...

Real-world validation of a multimodal LLM-powered pipeline for High-Accuracy Clinical Trial Patient Matching leveraging EHR data

Background: Patient recruitment in clinical trials is hindered by complex eligibility criteria and labor-intensive chart reviews. Prior research usi...

Inducing Causal Structure for Interpretable Neural Networks Applied to Glucose Prediction for T1DM Patients

Causal abstraction techniques such as Interchange Intervention Training (IIT) have been proposed to infuse neural network with expert knowledge enco...

AI-Driven Diabetic Retinopathy Diagnosis Enhancement through Image Processing and Salp Swarm Algorithm-Optimized Ensemble Network

Diabetic retinopathy is a leading cause of blindness in diabetic patients and early detection plays a crucial role in preventing vision loss. Tradit...

Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes.

BACKGROUND: Insulin resistance, lipotoxicity, and mitochondrial dysfunction contribute to the pathogenesis of metabolic dysfunction-associated steatot...

Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis

Accurate staging of Diabetic Retinopathy (DR) is essential for guiding timely interventions and preventing vision loss. However, current staging mod...

A Novel Ophthalmic Benchmark for Evaluating Multimodal Large Language Models with Fundus Photographs and OCT Images

In recent years, large language models (LLMs) have demonstrated remarkable potential across various medical applications. Building on this foundatio...

Precise Insulin Delivery for Artificial Pancreas: A Reinforcement Learning Optimized Adaptive Fuzzy Control Approach

This paper explores the application of reinforcement learning to optimize the parameters of a Type-1 Takagi-Sugeno fuzzy controller, designed to ope...

Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments

Uncovering causal mediation effects is of significant value to practitioners seeking to isolate the direct treatment effect from the potential media...

Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image Segmentation

The growing rate of chronic wound occurrence, especially in patients with diabetes, has become a concerning trend in recent years. Chronic wounds ar...

A Comparative Study of Diabetes Prediction Based on Lifestyle Factors Using Machine Learning

Diabetes is a prevalent chronic disease with significant health and economic burdens worldwide. Early prediction and diagnosis can aid in effective ...

Fusion of Various Optimization Based Feature Smoothing Methods for Wearable and Non-invasive Blood Glucose Estimation

Recently, the wearable and non-invasive blood glucose estimation approach has been proposed. However, due to the unreliability of the acquisition de...

GlucoLens: Explainable Postprandial Blood Glucose Prediction from Diet and Physical Activity

Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after meals, is a critical indicator of progression toward ...

Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis

Hypertensive retinopathy (HR) is a severe eye disease that may cause permanent vision loss if not diagnosed early. Traditional diagnostic methods ar...