Endocrinology

Machine learning techniques to predict diabetic ketoacidosis and HbA1c above 7% among individuals with type 1 diabetes - A large multi-centre study in Australia and New Zealand.

BACKGROUND AND AIMS: Type 1 diabetes and diabetic ketoacidosis (DKA) have a significant impact on individuals and society across a wide spectrum. Our objective was to utilize machine learning techniques to predict DKA and HbA1c>7 %.

Investigating factors influencing quality of life in thyroid eye disease: insight from machine learning approaches.

AIMS: Thyroid eye disease (TED) is an autoimmune orbital disorder that diminishes the quality of life (QOL) in affected individuals. Graves' ophthalmopathy (GO)-QOL questionnaire effectively assesses TED's effect on patients. This study aims to investigate the factors influencing visual functioning (QOL-VF) and physical appearance (QOL-AP) scores in Chinese TED patients using innovative data analy...

Deep learning model for automatic detection of different types of microaneurysms in diabetic retinopathy.

PURPOSE: This study aims to develop a deep-learning-based software capable of detecting and differentiating microaneurysms (MAs) as hyporeflective or ...

Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma.

This study aimed to develop and validate machine learning (ML) models to predict the occurrence of delayed hyponatremia after transsphenoidal surgery ...

A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles.

BACKGROUND: Glycogen storage disease (GSD) Ia is an ultra-rare inherited disorder of carbohydrate metabolism. Patients often present in the first mont...

The effect of renal function on the clinical outcomes and management of patients hospitalized with hyperglycemic crises.

BACKGROUND: The global prevalence of diabetes has been rising rapidly in recent years, leading to an increase in patients experiencing hyperglycemic c...

Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment.

INTRODUCTION: Treatment of type 2 diabetes (T2D) remains a significant challenge because of its multifactorial nature and complex metabolic pathways. ...

Discrimination of Benign and Malignant Thyroid Nodules through Comparative Analyses of Human Saliva Samples via Metabolomics and Deep-Learning-Guided Label-free SERS.

Thyroid nodules are a very common entity. The overall prevalence in the populace is estimated to be around 65-68%, among which a small portion (less t...

Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches.

Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However...

Predicting 28-day all-cause mortality in patients admitted to intensive care units with pre-existing chronic heart failure using the stress hyperglycemia ratio: a machine learning-driven retrospective cohort analysis.

Chronic heart failure (CHF) poses a significant threat to human health. The stress hyperglycemia ratio (SHR) is a novel metric for accurately assessin...

Exploring the effect of gestational diabetes mellitus on retinal vascular morphology by PKSEA-Net.

BACKGROUND: Gestational diabetes mellitus (GDM) is a temporary metabolic disorder in which small retinal vessels may have experience subtle changes be...

Comprehensive bioinformatics analysis identifies metabolic and immune-related diagnostic biomarkers shared between diabetes and COPD using multi-omics and machine learning.

BACKGROUND: Diabetes and chronic obstructive pulmonary disease (COPD) are prominent global health challenges, each imposing significant burdens on aff...

Enhancing repeatability of follicle counting with deep learning reconstruction high-resolution MRI in PCOS patients.

Follicle count, a pivotal metric in the adjunct diagnosis of polycystic ovary syndrome (PCOS), is often underestimated when assessed via transvaginal ...

Adversarial Exposure Attack on Diabetic Retinopathy Imagery Grading.

Diabetic Retinopathy (DR) is a leading cause of vision loss around the world. To help diagnose it, numerous cutting-edge works have built powerful dee...

Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning.

Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen s...

A data-driven machine learning algorithm to predict the effectiveness of inulin intervention against type II diabetes.

INTRODUCTION: The incidence of type 2 diabetes mellitus (T2DM) has increased in recent years. Alongside traditional pharmacological treatments, nutrit...

Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets.

INTRODUCTION: Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases do...

Interpretable machine learning models for predicting skip metastasis in cN0 papillary thyroid cancer based on clinicopathological and elastography radiomics features.

BACKGROUND: Skip lymph node metastasis (SLNM) in papillary thyroid cancer (PTC) involves cancer cells bypassing central nodes to directly metastasize ...

Empirical analysis on retinal segmentation using PSO-based thresholding in diabetic retinopathy grading.

OBJECTIVES: Diabetic retinopathy (DR) is associated with long-term diabetes and is a leading cause of blindness if it is not diagnosed early. The rapi...

Predicting and Ranking Diabetic Ketoacidosis Risk Among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.

To use electronic health record (EHR) data to develop a scalable and transferrable model to predict 6-month risk for diabetic ketoacidosis (DKA)-rela...