Endocrinology

Diabetes

Latest AI and machine learning research in diabetes for healthcare professionals.

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Showing 2101-2121 of 2,618 articles
SNER: Semi-Supervised Named Entity Recognition for Large Volume of Diabetes Data.

The medical literature and records on diabetes provide crucial resources for diabetes prevention and...

MCD-LightGBM System for Intelligent Analyzing Heterogeneous Clinical Drug Therapeutic Effects.

Causal effect estimation of individual heterogeneity is a core issue in the field of causal inferenc...

Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images.

OBJECTIVES: To develop and validate an automated diabetic macular oedema (DME) classification system...

Identification of novel therapeutic targets in hepatitis-B virus-associated membranous nephropathy using scRNA-seq and machine learning.

Hepatitis B Virus-associated membranous nephropathy (HBV-MN) significantly impacts renal health, par...

Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning.

Hypoglycemia is a serious complication in individuals with type 2 diabetes mellitus. Identifying who...

A machine learning-based risk prediction model for diabetic oral ulceration.

BACKGROUND: Diabetic oral ulceration (DOU) is a prevalent and debilitating complication among diabet...

Self-supervised model-informed deep learning for low-SNR SS-OCT domain transformation.

This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (S...

Leveraging Vision Transformers in Multimodal Models for Retinal OCT Analysis.

Optical Coherence Tomography (OCT) has become an indispensable imaging modality in ophthalmology, pr...

Sociodemographic Profile of People with Diagnosed Pancreatic Cancer in the UK: Retrospective Sentinel Network Cohort Study.

Pancreatic cancer is a devasting disease which is an increasing cause of cancer mortality. The aim o...

A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease.

BACKGROUND: Individuals with diabetic kidney disease (DKD) often suffer cardiac and kidney events. W...

Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.

Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant challenges to public h...

Effects of neonicotinoid pesticide exposure in the first trimester on gestational diabetes mellitus based on interpretable machine learning.

BACKGROUND: Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications an...

scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework.

Transcriptome-wide association studies (TWASs) help identify disease-causing genes but often fail to...

Optoelectronic-Coupled-Driven Microrobot for Biological Cargo Transport in Conductive Isosmotic Glucose Solution.

Electric field-driven micro/nanorobots, as micro/nanodevices with autonomous motion capability, have...

Elucidating the Prognostic and Therapeutic Implications of Insulin Resistance Genes in Breast Cancer: A Machine Learning-Powered Analysis.

Breast cancer (BC) is among the most prevalent malignancies and remains the leading cause of cancer-...

Predicting dementia in people with Parkinson's disease.

Parkinson's disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experien...

Ethics of Artificial Intelligence in Medicine and Ophthalmology.

BACKGROUND: This review explores the bioethical implementation of artificial intelligence (AI) in me...

Next-generation Approaches in Targeting Polycystic Ovarian Syndrome: Innovative Strategies.

Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder that affects millions of women worl...

Application of interpretable machine learning algorithms to predict macroangiopathy risk in Chinese patients with type 2 diabetes mellitus.

Macrovascular complications are leading causes of morbidity and mortality in patients with type 2 di...

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