Endocrinology

The triglyceride-glucose index and its obesity-related derivatives as predictors of all-cause and cardiovascular mortality in hypertensive patients: insights from NHANES data with machine learning analysis.

BACKGROUND: Hypertension (HTN) is a global public health concern and a major risk factor for cardiovascular disease (CVD) and mortality. Insulin resistance (IR) plays a crucial role in HTN-related metabolic dysfunction, but its assessment remains challenging. The triglyceride-glucose (TyG) index and its derivatives (TyG-BMI, TyG-WC, and TyG-WHtR) have emerged as reliable IR markers. In this study,...

Optimizing warfarin dosing in diabetic patients through BERT model and machine learning techniques.

This study highlights the importance of evaluating warfarin dosing in diabetic patients, who require careful anticoagulation management. With rising rates of diabetes and cardiovascular diseases, understanding the factors influencing warfarin therapy is vital for improving patient outcomes and reducing adverse events. Data was sourced from the IWPC dataset, examining characteristics such as age, g...

Corrigendum: A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study.

[This corrects the article DOI: 10.3389/fendo.2024.1479442.].

A safe-enhanced fully closed-loop artificial pancreas controller based on deep reinforcement learning.

Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden...

Acute effect of endurance exercise on human milk insulin concentrations: a randomised cross-over study.

INTRODUCTION: Insulin is present in human milk and its concentration correlates with maternal circulating levels. Studies on the association between h...

Machine Learning Model for Risk Stratification of Papillary Thyroid Carcinoma Based on Radiopathomics.

RATIONALE AND OBJECTIVES: This study aims to develop a radiopathomics model based on preoperative ultrasound and fine-needle aspiration cytology (FNAC...

A robust and generalized framework in diabetes classification across heterogeneous environments.

Diabetes mellitus (DM) represents a major global health challenge, affecting a diverse range of demographic populations across all age groups. It has ...

Efficient diagnosis of diabetes mellitus using an improved ensemble method.

Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been w...

Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.

Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of...

Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.

Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicti...

Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.

PURPOSE: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during...

Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.

INTRODUCTION: Type 2 diabetes (T2D) is a complex metabolic disorder with significant global health implications. Understanding the molecular mechanism...

Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study.

BACKGROUND: Type 2 diabetes (T2D) is a leading cause of premature morbidity and mortality globally and affects more than 100 million people in the wor...

Deep learning based prediction of depression and anxiety in patients with type 2 diabetes mellitus using regional electronic health records.

BACKGROUND: Depression and anxiety are prevalent mental health conditions among individuals with type 2 diabetes mellitus (T2DM), who exhibit unique v...

Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease.

Diabetes mellitus is a chronic metabolic disease that increasingly affects people every year. It is known that with its progression and poor managemen...

AI Machine Learning-Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis.

BACKGROUND: Diabetes is prevalent in older adults, and machine learning algorithms could help predict diabetes in this population.

Interpreting IGF-1 in children treated with recombinant growth hormone: challenges during early puberty.

OBJECTIVE: It can be challenging to determine the correct dosage of recombinant growth hormone (GH) in children with GH deficiency, leading to highly ...

Predictors of glycaemic improvement in children and young adults with type 1 diabetes and very elevated HbA1c using the MiniMed 780G system.

AIMS: This study aimed to identify key factors with the greatest influence on glycaemic outcomes in young individuals with type 1 diabetes (T1D) and v...

Perspective: Multiomics and Artificial Intelligence for Personalized Nutritional Management of Diabetes in Patients Undergoing Peritoneal Dialysis.

Managing diabetes in patients on peritoneal dialysis (PD) is challenging due to the combined effects of dietary glucose, glucose from dialysate, and o...

Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique.

Cardio Vascular Disease (CVD) is one of the leading causes of mortality and it is estimated that 1 in 4 deaths happens due to it. The disease prevalen...