Obesity

Comprehensive evaluation of health status of Tibetan primary and middle school students based on an artificial neural network model: A cross-sectional study.

The high-altitude and unique climatic conditions of Tibet can have a significant impact on the growth and development of local children and adolescents. We aimed to apply an artificial neural network model to evaluate the physical health status of primary and secondary school students aged 7 to 18 years in Shigatse, Tibet, China. A cross-sectional study was conducted among eligible primary and sec...

The next frontier: AI, multi-omics, and predictive models for weight gain in HIV.

PURPOSE OF REVIEW: People with HIV (PWH) are increasingly susceptible to excess weight gain and obesity after initiation of antiretroviral therapy. However, there is substantial variation in individual weight gain that is difficult to predict with clinical factors alone. We review emerging methods in machine learning and multi-omics that address the biology and prediction of weight gain and weight...

Associations of Iron Indicators with Cognitive Impairment Risk in Postmenopausal Women: Evidence from a Matched Case-Control Study and Back Propagation Neural Network Model.

Iron is essential for normal cognitive function, and women have nearly twice the age-matched prevalence of cognitive impairment compared with men. The...

AI-Derived Score to Predict Effort Intolerance and Adverse Outcome across the Heart Failure Spectrum.

AIMS: To develop and externally validate an artificial intelligence (AI)-driven model to predict effort intolerance (i.e., peak oxygen uptake [VO₂] <1...

Identification of weight loss predictors using machine learning approaches in adolescents with obesity.

BACKGROUND: Despite the effectiveness of lifestyle multidisciplinary (LMD) weight loss interventions in pediatric obesity, outcomes remain variable be...

Research on artificial intelligence in stress and obesity: A bibliometric analysis using scopus, web of science, PubMed.

OBJECTIVE: Stress and obesity are major health concerns affecting individuals worldwide. Artificial Intelligence (AI) is being designed to identify an...

A Machine Learning-Based Model to Predict Overactive Bladder Risk Among US Women: Evidence From the National Health and Nutrition Examination Survey 2011-2018.

BACKGROUND: Overactive bladder (OAB) is a prevalent condition, particularly among women, characterized by urinary urgency, often accompanied by freque...

From voxel discovery to regional interaction: A multi-level interpretable framework for Alzheimer's disease diagnosis.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder where early diagnosis serves as the only viable window for effective interventi...

Machine learning for population-level risk prediction of future cholangiocarcinoma.

BACKGROUND: The poor prognosis of cholangiocarcinoma (CCA) is largely driven by rapid, asymptomatic disease progression, which usually results in a la...

Machine Learning Models Integrating CYP2C19 Genotypes for Long-Term Risk Prediction and Personalized Antiplatelet Therapy Selection in Myocardial Infarction.

Machine learning (ML) models integrating genetic and clinical data show promise for personalizing antiplatelet therapy after myocardial infarction (MI...

Correlates of muscle strengthening exercise in adolescents: A machine learning based analysis.

BACKGROUND: Muscle-strengthening exercise (MSE) is a critical component of adolescent health, yet its correlates remain less understood than those of ...

Scalable prediction of suicidal risk in university students: a three steps machine learning approach in university settings.

Suicide is a leading cause of death among young adults, with university students representing a particularly vulnerable subgroup. Although prevention ...

Identification of critical brain regions for young adults with obesity and their relationships with impulsivity using machine learning based on neuroanatomical features.

BACKGROUND: Prior obesity neuroimaging studies used univariate methods and small samples, limiting reproducibility. Employing a large-scale dataset an...

Predictive Modelling of 3-Month Glycemic Target Achievement in Type 2 Diabetes Mellitus Using Artificial Intelligence and Continuous Glucose Monitoring: A Retrospective Cohort Study.

AIMS: To develop and validate a machine learning model incorporating continuous glucose monitoring (CGM) metrics to predict 3-month glycemic target ac...

MAFLD/MASLD: Past, Present, and Future.

Metabolic dysfunction-associated steatotic liver disease (MASLD), also known as nonalcoholic fatty liver disease and metabolic dysfunction-associated ...

Computable Phenotype for Identifying Undiagnosed Hypermobile Ehlers-Danlos Syndrome: Protocol for a Development and Validation Study.

BACKGROUND: Hypermobile Ehlers-Danlos syndrome (hEDS) is a multisystemic hereditary connective tissue disorder characterized by generalized joint hype...

Machine learning-based prediction of intraoperative blood transfusion in major surgery: exploiting clinical variables and systemic inflammatory indices.

BACKGROUND: Intraoperative blood transfusion is common in major surgery. Predicting transfusion risk may improve perioperative management, optimize bl...

Comparative Performance of AI Models and Clinicians in Evidence-Based Cardiovascular Disease Management for People Living With HIV: Comparative Study.

BACKGROUND: Although widespread antiretroviral therapy has extended the life expectancy of people living with HIV, cardiovascular disease (CVD) has em...

Network Analysis-Driven Machine Learning Model for Identifying High-Cost Stroke Inpatients Using Hospital Discharge Data: Retrospective Study.

BACKGROUND: The medical burden caused by stroke is increasingly severe, and a small minority of high-cost patients consume the majority of medical exp...

Social Media as a Driver of Obesity in Children and Adolescents (Aged 6-18 Years): It Is Time for Regulatory Action.

Obesity in children and adolescents is rising in China and globally, with health consequences that are already evident during childhood. This Viewpoin...