Obesity

Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.

INTRODUCTION: Prediabetes is a prevalent condition in which early detection and lifestyle interventions can prevent or delay progression to diabetes. Artificial intelligence (AI) and machine learning (ML) offer enhanced tools for diagnosis, risk stratification, and scalable delivery of lifestyle interventions. This review synthesizes current applications of AI/ML in patients with prediabetes.

Improving the Readability of Institutional Heart Failure-Related Patient Education Materials Using GPT-4: Observational Study.

BACKGROUND: Heart failure management involves comprehensive lifestyle modifications such as daily weights, fluid and sodium restriction, and blood pressure monitoring, placing additional responsibility on patients and caregivers, with successful adherence often requiring extensive counseling and understandable patient education materials (PEMs). Prior research has shown PEMs related to cardiovascu...

The Impact of Genetic and Acquired Risk Factors on Thromboembolic Events: A Retrospective Study.

This study, involving a cohort of 980 patients with arterial and/or venous events, evaluated the relative importance of genetic and traditional risk f...

AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view.

This study aims to enhance the dosimetry accuracy in I planar imaging by utilizing a single oblique view and Monte Carlo (MC) validated dose point ker...

Data-driven nutritional assessment of urban food landscapes: insights from Boston, London, and Dubai.

Urban food landscapes significantly influence dietary habits and health outcomes, with disparities in food access contributing to obesity, particularl...

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.

Advanced metabolic-dysfunction-associated steatotic liver disease (MASLD) fibrosis (F3-4) predicts liver-related outcomes. Serum and elastography-base...

Artificial intelligence and digital twins for the personalised prediction of hypertension risk.

Hypertension is a significant global health challenge, contributing substantially to morbidity and mortality through its association with various card...

From combination early detection to multicancer testing: shifting cancer care toward proactive prevention and interception.

Identifying the presence of tumors at a very early stage or deciphering the process underlying their development can enable the interception of pro-ma...

Unveiling Quality of Life Factors for the Elderly: A Public Health Nursing Approach Enhanced by Advanced ML and DL Techniques.

Community health nurses can enhance the elderly's quality of life (QoL) through personalized care, lifestyle counselling, and preventive measures. The...

Integrating artificial intelligence in healthcare: applications, challenges, and future directions.

Artificial intelligence (AI) has demonstrated remarkable potential in transforming medical diagnostics across various healthcare domains. This paper e...

Developing a Panel of Shared Susceptibility Genes as Diagnostic Biomarkers for chronic obstructive pulmonary disease and Heart Failure.

AIM: Chronic obstructive pulmonary disease (COPD) and heart failure (HF) are closely intertwined comorbidities that present significant clinical chall...

Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015-2018.

Cardiometabolic Multimorbidity (CMM) has emerged as one of the primary threats to human health globally due to its high incidence, disability, and mor...

The value of triglyceride-glucose index-related indices in evaluating migraine: perspectives from multi-centre cross-sectional studies and machine learning models.

BACKGROUND: This study employed representative data from the U.S. and China to delve into the correlation among migraine prevalence, the triglyceride‒...

A model based on artificial intelligence for the prediction, prevention and patient-centred approach for non-communicable diseases related to metabolic syndrome.

Metabolic syndrome (MetS) is related to non-communicable diseases (NCDs) such as type 2 diabetes (T2D), metabolic-associated steatotic liver disease (...

Classification of Japanese type 1 diabetes based on clinical phenotypes and its association with diabetic complications: Across-sectional study.

INTRODUCTION: Despite the increasing number of studies using machine learning to develop individualized treatment strategies, only a few have been con...

Integrating machine learning and bioinformatics approaches to identify novel diagnostic gene biomarkers for diabetic mice.

Diabetes is a complex metabolic disorder, and its pathogenesis involves the interplay of genetic, environmental factors, and lifestyle choices. With t...

Assessment of drug induced hyperuricemia and gout risk using the FDA adverse event reporting system.

Hyperuricemia, the key pathological basis of gout, is increasingly prevalent worldwide. While lifestyle factors contribute, various medications also p...

Interactive SBOT for Health Education and Active Living.

Physical inactivity is a global health issue contributing to chronic conditions like obesity and cardiovascular diseases, with regular exercise often ...

Therapeutic horizons in metabolic dysfunction-associated steatohepatitis.

Metabolic dysfunction-associated steatohepatitis (MASH), the progressive inflammatory form of MASLD, is now a leading cause of chronic liver disease w...

Machine learning based obesity and aging related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.

BACKGROUND: Stomach adenocarcinoma (STAD) is one of most common cancers with high invasiveness and poor prognosis. Obesity and aging are correlated wi...