Obesity

Building an AI-Powered Educational Tool for Exploring Microbial Relationships in Parkinson’s Disease

This paper presents the Neurobiome Navigator, an AI-powered, highly interactive, and easily navigable application designed to help users explore the complex relationships between the human microbiome and Parkinson’s disease (PD). The app focuses on the gut microbiome and the oral microbiome, known to have a strong relationship with PD, as well as impulse control disorders (ICD), a significant non-...

Altered microbial carbohydrate metabolism is associated with anxiety and gastrointestinal symptoms in patients with Generalized Anxiety Disorder

Generalized anxiety disorder (GAD) is a common psychiatric condition, with unknown etiology and pathophysiology. Recent studies have suggested alterations in the microbiota-gut-brain axis may be involved in the development of GAD. We aimed to explore the interactions between the gut microbiota, gastrointestinal and psychiatric symptoms, neuroimmune markers and dietary patterns in patients with GAD...

Personalized AI Prompt Generator and ChatGPT for Weight Loss: Randomized Controlled Trial in Adults with Overweight and Obesity

The global prevalence of overweight and obesity continues escalating, driven by environmental factors and lifestyle behaviors leading to cardiovascula...

Enhanced Diabetes Prediction Using Novel Additive-Multiplicative Neural Networks: A Comprehensive Machine Learning Analysis of the PIMA Indians Dataset

Early diabetes detection remains challenging, requiring robust machine learning approaches that balance accuracy with clinical interpretability for ef...

Machine learning and natural language processing for the early detection of potential mental disorders among school-age children: a prospective birth cohort study

Early detection of childhood mental health disorders remains challenging due to gaps in current screening approaches that lack sensitivity to subtle p...

Compartment-specific Fat Distribution Profiles have Distinct Relationships with Cardiovascular Ageing and Future Cardiovascular Events

Obesity is a global public health priority and a major risk factor for cardiovascular disease (CVD). Emerging evidence indicates variation in patholog...

Non-Traditional Lipid Ratios Predict Cardiovascular-Kidney-Metabolic Syndrome: Insights from Machine Learning Model Using NHANES Data

Cardiovascular-kidney-metabolic (CKM) syndrome is a newly defined multisystem disease continuum characterized by the coexistence of metabolic dysfunct...

Evaluation of Easy-to-Implement Intervention for Menstrual Pain in a Series of N-of-1 Trials: Study Protocol of the Menstrual Pain Intervention Among Students Study (MPIS)

Dysmenorrhea, or menstrual pain, is a prevalent issue among female university students that negatively influences their productivity, academic perform...

Comprehensive, Transparent, and Fair Machine Learning Models for Hypertension Risk Prediction: Benchmarking With Framingham, External Validation, Individual-Level Analysis, and Equitable Clinical Utility

Hypertension (HTN) is a leading, yet often underdiagnosed, cause of cardiovascular diseases worldwide. While clinical risk scores like the Framingham ...

Internal and External Validation of Machine Learning Algorithms Versus FINDRISC for Incident Type 2 Diabetes: A Transparent, Explainable Benchmark Using SHAP

Type 2 diabetes mellitus (T2DM) affects almost half a billion people, and the projected cost is $2.25 trillion by 2030; early detection strategies are...

Predictive Modelling of Depression Treatment Response using Individual Symptoms and Latent Factors

Machine learning models have increasingly been used to identify predictors of treatment response in depression, and it is hoped that they may eventual...

Development of Self-Assessment Tools for Osteoporosis among Postmenopausal Vietnamese Women: A Machine Learning Approach

Osteoporosis is a major health concern in Vietnam due to a rise in aging rates. However, cost-effective early screening tools tailored to the Vietname...

Body composition and melanoma incidence risk: insights from a longitudinal lung cancer screening cohort

This study explored the association between low-dose computed tomography (LDCT)-derived body composition and melanoma incidence risk. LDCT scans from ...

ALTARN: A Tabular Residual Neural Network for Alzheimer’s Disease Classification and Prediction

Early and accurate prediction of Alzheimer’s disease (AD) from accessible clinical data remains a significant challenge in healthcare. This study prop...

Dietary Macronutrient Intake and the Gut Microbiome in Adults Undergoing Bariatric Surgery for Obesity

Limited information linking dietary intake to gut metagenomic data in bariatric surgery patients is available. We examined whether there were correlat...

Optimizing Dose-Response Decisions in Psoriatic Arthritis via Causal Machine Learning: A Real-World Evaluation of Secukinumab Treatment

Personalized treatment in psoriatic arthritis (PsA) remains challenging, particularly in guiding dose escalation decisions. We applied a causal machin...

From claims to care: Machine learning algorithm to classify urinary tract infection cases using Swiss health insurance data

To evaluate whether machine learning (ML) applied to comprehensive claims data without diagnostic codes can distinguish a high proportion of antibioti...

Risk assessment in cardiac surgery: Exploring machine learning and laboratory indices as adjunctive tools

Post-operative outcomes of cardiovascular surgery vary greatly among patients for a variety of reasons. While the specific reasons are often multifact...

Individualized Therapy Optimization for Type 2 Diabetes

Type 2 diabetes is a wide-spread chronic condition in which blood glucose and body weight management constitute essential therapeutic targets. Emergin...

Advancing cardiovascular disease risk prediction beyond conventional methods: a systematic review of multimodal machine learning models integrating traditional clinical factors and multi-omics data

Cardiovascular disease (CVD) is a leading global health burden. Traditional risk prediction models, though widely used, often overlook genetic predisp...