Endocrinology

Primary care research on hypertension: A bibliometric analysis using machine-learning.

Hypertension is one of the most important chronic diseases worldwide. Hypertension is a critical condition encountered frequently in daily life, forming a significant area of service in Primary Health Care (PHC), which healthcare professionals often confront. It serves as a precursor to many critical illnesses and can lead to fatalities if not addressed promptly. Our study underscores the importan...

Leveraging Gene Expression Data and Explainable Machine Learning for Enhanced Early Detection of Type 2 Diabetes

Diabetes, particularly Type 2 diabetes (T2D), poses a substantial global health burden, compounded by its associated complications such as cardiovascular diseases, kidney failure, and vision impairment. Early detection of T2D is critical for improving healthcare outcomes and optimizing resource allocation. In this study, we address the gap in early T2D detection by leveraging machine learning (M...

Artificial Intelligence Chatbots in Patient Communication: Current Possibilities.

ChatGPT, an artificial intelligence (AI) chatbot, can generate text prompts based on user input. This study investigated the possibility of utilizing ...

m6A-related genes and their role in Parkinson's disease: Insights from machine learning and consensus clustering.

Parkinson disease (PD) is a chronic neurological disorder primarily characterized by a deficiency of dopamine in the brain. In recent years, numerous ...

Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-Series

Accurately predicting blood glucose (BG) levels of ICU patients is critical, as both hypoglycemia (BG < 70 mg/dL) and hyperglycemia (BG > 180 mg/dL)...

Development and Validation of Machine Learning Models for Identifying Prediabetes and Diabetes in Normoglycemia.

BACKGROUND: Prediabetes and diabetes are both abnormal states of glucose metabolism (AGM) that can lead to severe complications. Early detection of AG...

Predicting Pancreatic Cancer in New-Onset Diabetes Cohort Using a Novel Model With Integrated Clinical and Genetic Indicators: A Large-Scale Prospective Cohort Study.

INTRODUCTION: Individuals who develop new-onset diabetes have been identified as a high-risk cohort for pancreatic cancer (PC), exhibiting an incidenc...

MixEHR-Nest: Identifying Subphenotypes within Electronic Health Records through Hierarchical Guided-Topic Modeling

Automatic subphenotyping from electronic health records (EHRs)provides numerous opportunities to understand diseases with unique subgroups and enhan...

Machine learning approach to brain tumor detection and classification

Brain tumor detection and classification are critical tasks in medical image analysis, particularly in early-stage diagnosis, where accurate and tim...

Optimization and Application of Cloud-based Deep Learning Architecture for Multi-Source Data Prediction

This study develops a cloud-based deep learning system for early prediction of diabetes, leveraging the distributed computing capabilities of the AW...

Modeling Alzheimer's Disease: From Memory Loss to Plaque & Tangles Formation

We employ the Hopfield model as a simplified framework to explore both the memory deficits and the biochemical processes characteristic of Alzheimer...

GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks

The rising rates of diabetes necessitate innovative methods for its management. Continuous glucose monitors (CGM) are small medical devices that mea...

Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review.

The burden of undiagnosed diabetes mellitus (DM) is substantial, with approximately 240 million individuals globally unaware of their condition, dispr...

Reducing Overtreatment of Indeterminate Thyroid Nodules Using a Multimodal Deep Learning Model

Objective: Molecular testing (MT) classifies cytologically indeterminate thyroid nodules as benign or malignant with high sensitivity but low positi...

Expert-level vision-language foundation model for real-world radiology and comprehensive evaluation

Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in me...

Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for elucidating cellular heterogeneity and tissue function in various biological contexts. H...

Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management

Diabetes is a chronic disease with a significant global health burden, requiring multi-stakeholder collaboration for optimal management. Large langu...

Integrating Bayesian Approaches and Expert Knowledge for Forecasting Continuous Glucose Monitoring Values in Type 2 Diabetes Mellitus

Precise and timely forecasting of blood glucose levels is essential for effective diabetes management. While extensive research has been conducted o...

Effect of Clinical History on Predictive Model Performance for Renal Complications of Diabetes

Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy, which, in turn, is the leading cause of end-stage chr...

Comparison of Epilepsy Induced by Ischemic Hypoxic Brain Injury and Hypoglycemic Brain Injury using Multilevel Fusion of Data Features

The study aims to investigate the similarities and differences in the brain damage caused by Hypoxia-Ischemia (HI), Hypoglycemia, and Epilepsy. Hypo...