Primary Care

Obesity classification: a comparative study of machine learning models excluding weight and height data.

OBJECTIVE: Obesity is a global health problem. The aim is to analyze the effectiveness of machine learning models in predicting obesity classes and to determine which model performs best in obesity classification.

ChatGPT-4o and 4o1 Preview as Dietary Support Tools in a Real-World Medicated Obesity Program: A Prospective Comparative Analysis.

BACKGROUND/OBJECTIVES: Clinicians are becoming increasingly interested in the use of large language models (LLMs) in obesity services. While most experts agree that LLM integration would increase access to obesity care and its efficiency, many remain skeptical of their scientific accuracy and capacity to convey human empathy. Recent studies have shown that ChatGPT-3 models are capable of emulating...

Re-imagining human cell culture media: Challenges, innovations, and future directions.

The development of optimized culture media is pivotal to advancements in human cell culture, underpinning progress in regenerative medicine, cell ther...

Surrogate markers of insulin resistance and coronary artery disease in type 2 diabetes: U-shaped TyG association and insights from machine learning integration.

BACKGROUND: Surrogate insulin resistance (IR) indices are simpler and more practical alternatives to insulin-based IR indicators for clinical use. Thi...

Optimized hybrid machine learning framework for early diabetes prediction using electrogastrograms.

In recent years, diabetes has become a global public health problem, and it is reported that the migrant Indians have more prevalence rate of Type-II ...

Three-dimensional markerless surface topography approach with convolutional neural networks for adolescent idiopathic scoliosis screening.

Adolescent idiopathic scoliosis (AIS) is a three-dimensional lateral and torsional deformity of the spine, affecting up to 5% of the population. Tradi...

Machine learning based model for the early detection of Gestational Diabetes Mellitus.

BACKGROUND: Gestational Diabetes Mellitus (GDM) is one of the most common medical complications during pregnancy. In the Gulf region, the prevalence o...

Advanced SERSome-based artificial-intelligence technology for identifying medicinal and edible homologs.

Medicinal and edible homologs (MEHs) offer significant preventive and therapeutic benefits for various diseases and health functions. However, the wid...

A Novel Mouse Model of Type 2 Diabetes Using a Medium-Fat Diet, Fructose, and Streptozotocin to Study the Complications of Human Disease.

The study of type 2 diabetes mellitus (T2DM) pathophysiology relies mainly on the use of animal models, the most common of which involves the consumpt...

Identifying individuals at risk for weight gain using machine learning in electronic medical records from the United States.

AIMS: Numerous risk factors for the development of obesity have been identified, yet the aetiology is not well understood. Traditional statistical met...

Using Machine Learning Models to Diagnose Chronic Rhinosinusitis: Analysis of Pre-Treatment Patient-Generated Health Data to Predict Cardinal Symptoms and Sinonasal Inflammation.

BackgroundThe diagnosis of chronic rhinosinusitis (CRS) relies upon patient-reported symptoms and objective nasal endoscopy and/or computed tomography...

Sensing the Future of Thrombosis Management: Integrating Vessel-on-a-Chip Models, Advanced Biosensors, and AI-Driven Digital Twins.

Thrombotic events, such as strokes and deep vein thrombosis, remain a significant global health burden, with traditional diagnostic methods often fail...

Evaluation of factors associated with adult skeletal fluorosis in coal-burning type of endemic fluorosis and initial screening model based on machine learning in Guizhou, Southwest China.

Skeletal fluorosis caused by coal-burning type endemic fluorosis greatly affects the health of the population in the affected areas, but large-scale d...

A machine learning approach to predict mortality and neonatal persistent pulmonary hypertension in newborns with congenital diaphragmatic hernia. A retrospective observational cohort study.

UNLABELLED: Congenital diaphragmatic hernia (CDH) has high morbidity and mortality rates. This study aimed to develop a machine learning (ML) algorith...

Prediction of Hypertension in the Pediatric Population Using Machine Learning and Transfer Learning: A Multicentric Analysis of the SAYCARE Study.

OBJECTIVE: To develop a machine learning (ML) model utilizing transfer learning (TL) techniques to predict hypertension in children and adolescents ac...

A multimodal approach to depression diagnosis: insights from machine learning algorithm development in primary care.

General practitioners play an essential role in identifying depression and are often the first point of contact for patients. Current diagnostic tools...

Early prediction of cardiovascular events following treatments in female breast cancer patients: Application of real-world data and artificial intelligence.

• Application of real-world data and artificial intelligence in detecting cardiotoxicity following cancer treatment. • Clinical features have been use...

Innovative construction and application of bile duct organoids: Unraveling the complexity of bile duct diseases and potential therapeutic strategies.

The biliary system is crucial for liver function, regulating bile production, secretion, and transport. Dysfunctions within this system can lead to va...

Nanomaterial Innovations and Machine Learning in Gas Sensing Technologies for Real-Time Health Diagnostics.

Breath sensors represent a frontier in noninvasive diagnostics, leveraging the detection of volatile organic compounds (VOCs) in exhaled breath for re...

Intelligent risk stratification of hypertension based on ambulatory blood pressure monitoring and machine learning algorithms.

. Risk stratification of hypertension plays a crucial role in the treatment decisions and medication guidance during clinical practices. Although frui...