Exercise & Fitness

Development and Validation of a parsimonious AI-Based Risk Score for Mortality in Heart Failure: A UK cohort study

Accurate risk stratification in heart failure (HF) is crucial to guide clinical decisions, optimise therapeutic strategies and inform resource allocation. Existing widely used tools like from the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) show modest discriminatory performance and rely on specialised tests (e.g., echocardiography), while advanced Artificial Intelligence (AI) mode...

Optimized Machine Learning Algorithms for the Classification and Diagnosis of Sleep Disorders

Sleep disorders, including insomnia and obstructive sleep apnea, affect millions of individuals worldwide but are frequently undetected due to the high cost, limited availability, and complexity of conventional diagnostic tools such as polysomnography. This study presents an interpretable machine learning framework for multi-class sleep disorder classification that utilizes routine clinical and li...

A Metabolic-Inflammatory Phenotype of Pelvic Floor Dysfunction: A Machine Learning-Based Discovery in a Nationally Representative U.S. Cohort

Pelvic floor dysfunction (PFD) is a highly prevalent and heterogeneous condition among women. The traditional view of PFD as a single clinical entity ...

A Drug Repurposing Strategy for a New Cause of Endometrial Infertility: Unveiling Promising New Treatments

Which mechanisms of action and candidate drugs can be used to treat endometrial failure caused by molecular alterations rather than endometrial timing...

Comparing different types of machine learning models in diagnosing diabetes mellitus utilizing electrocardiography and clinical data

Diabetes Mellitus (DM) represents one of the most significant global public health challenges of the 21st century. This dramatic increase in the preva...

The Silent Signal: Unmasking Myocardial Ischemia in a Resting Heartbeat with Machine Learning

Ischemic heart disease (IHD) remains the leading cause of morbidity and mortality worldwide, imposing a staggering burden on healthcare systems and so...

Prevalence and Predictors of Silent Vertebral Compression Fractures: A Cross-Sectional Population-Based Study Using UK Biobank Imaging Data

To estimate the prevalence of silent vertebral compression fractures (VCF) in an asymptomatic population and to assess the demographic and clinical pr...

Distinct Features of Predictive Profiles for Post-TAVR Functional Improvement and Mortality. Value of 6-minute Walk Test and Myocardial Work Analysis

Clinical improvement and survival benefit after transcatheter aortic valve replacement (TAVR) are difficult to predict. Despite the identification of ...

Prediction of Long COVID and Mortality among Patients with Substance Use Disorder

The convergence of the COVID-19 pandemic and the substance use disorder (SUD) crisis has created a syndemic that places this vulnerable population at ...

The Role of Food Delivery Apps in Transforming Urban Eating Patterns: A Comprehensive Behavioral and Mathematical Analysis of Swiggy and Zomato Ecosystems

The proliferation of food delivery applications has fundamentally transformed urban dietary behaviors, creating unprecedented shifts in meal consumpti...

Artificial Intelligence Significantly Improves Adenoma Detection Rate but Does Not Affect Polyp Detection Rate in Colonoscopy: A Propensity Score Matching Study

Colorectal cancer (CRC) remains a major cause of cancer-related morbidity and mortality worldwide. Endoscopy and adenoma removal are effective in redu...

Characterize Disease Progression Subphenotypes in Real World Populations with Overweight and Obesity using a Graph-based Neural Network Framework

Obesity is a chronic, heterogeneous condition, with risks, trajectories, and treatment responses that vary widely among individuals. However, research...

Personalized Machine Learning guided Intervention for Optimizing Lifestyle Behaviors in Depression

Personalized data-driven interventions for depression are much needed. Here, we leveraged N-of-1 machine learning (ML) to optimally target behavioral ...

Predicting Alzheimer’s Disease Diagnosis, a Decade or more Years before Onset using the Electronic Health Record and Random Forest Machine Learning Models

There is need to detect and intervene in pre-clinical phases of Alzheimer’s disease (AD). Electronic health records (EHRs) may help predict AD using m...

Integrating Infection Burden and Multimodal Biomarkers for Early Detection of Alzheimers Disease: A Sheaf-ML Framework

Alzheimers disease (AD) remains a major global health challenge, with growing evidence linking chronic infections, immune aging, and neurodegeneration...

Incorporating Dietary Information to Enhance Polygenic Prediction Models with Applications to Body Mass Index and Type 2 Diabetes

Polygenic predictors can enhance screening for biomedical conditions, such as metabolism-related traits and diseases, but explain limited phenotypic v...

FusionAge framework for multimodal machine learning-based aging clocks uncovers cardiorespiratory fitness as a major driver of aging and inflammatory drivers of aging in response to spaceflight

Traditional epigenetic aging clocks are limited because they do not incorporate clinical information and functional tests, and rely on DNA samples and...

Genetic and Etiological Insights from Automated Lumen Diameter Measurements in Carotid Ultrasounds of the UK Biobank

Carotid ultrasound is routinely used in clinical practice for non-invasive vascular anatomical and functional assessment. In particular, the carotid i...

Physical Activity Shapes Brain Structure, Function, and the Computational Mechanisms of Cognitive Control

Sedentarism is prevalent and associated with poorer mental and physical health. Whether everyday physical activity (PA) maps onto computational decisi...

Advancing Cardiovascular Disease Diagnosis with an Interpretable and Responsible AI Framework

Cardiovascular disease (CVD) remains a leading global health threat, responsible for one in five deaths worldwide. Early detection is critical to miti...