Endocrinology

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

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Machine-Learning-Guided Peptide Drug Discovery: Development of GLP-1 Receptor Agonists with Improved Drug Properties.

Peptide-based drug discovery has surged with the development of peptide hormone-derived analogs for ...

Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning.

BACKGROUND AND AIMS: The recurrence of papillary thyroid carcinoma (PTC) is not unusual and associat...

Causal inference and machine learning in endocrine epidemiology.

With the rapid development of computer science, there is an increasing demand for the use of causal ...

Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality.

Understanding the combined effects of risk factors on all-cause mortality is crucial for implementin...

Estimating the prevalence of diabetic retinopathy in electronic health records with massive missing labels.

OBJECTIVE: The paper aims to address the problem of massive unlabeled patients in electronic health ...

Artificial intelligence derived categorizations significantly improve HOMA IR/β indicators: Combating diabetes through cross-interacting drugs.

Improvements in the homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis mod...

Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic Segmentation Model.

Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume o...

Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis.

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by ab...

An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems.

In hybrid automatic insulin delivery (HAID) systems, meal disturbance is compensated by feedforward ...

A deep neural network prediction method for diabetes based on Kendall's correlation coefficient and attention mechanism.

Diabetes is a chronic disease, which is characterized by abnormally high blood sugar levels. It may ...

Application of Proteomics and Machine Learning Methods to Study the Pathogenesis of Diabetic Nephropathy and Screen Urinary Biomarkers.

Diabetic nephropathy (DN) has become the main cause of end-stage renal disease worldwide, causing si...

A machine learning tool for identifying patients with newly diagnosed diabetes in primary care.

BACKGROUND AND AIM: It is crucial to identify a diabetes diagnosis early. Create a predictive model ...

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening.

Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluid...

Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts.

This study aimed to develop and validate a machine learning (ML) model tailored to the Korean popula...

Exploring the diagnostic performance of machine learning in prediction of metabolic phenotypes focusing on thyroid function.

In this study, we employed various machine learning models to predict metabolic phenotypes, focusing...

Four Genes with Seven Targeted Drugs might be Treatment for Diabetic Nephropathy and Acute Coronary Syndrome.

Diabetes nephropathy (DN) is a main risk factor for acute coronary syndrome (ACS), but the molecular...

Optical imaging for diabetic retinopathy diagnosis and detection using ensemble models.

Diabetes, characterized by heightened blood sugar levels, can lead to a condition called Diabetic Re...

Supervised Machine Learning-Based Models for Predicting Raised Blood Sugar.

Raised blood sugar (hyperglycemia) is considered a strong indicator of prediabetes or diabetes melli...

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