Obesity

Physiological model-based machine learning for classifying patients with binge-eating disorder (BED) from the Oral Glucose Tolerance Test (OGTT) curve.

BACKGROUND AND OBJECTIVE: Binge eating disorder (BED) is the most frequent eating disorder, often confused with obesity, with which it shares several characteristics. Early identification could enable targeted therapeutic interventions. In this study, we propose a hybrid pipeline that, starting from plasma glucose data acquired during the Oral Glucose Tolerance Test (OGTT), allows us to classify t...

Identification of metabolism related biomarkers in obesity based on adipose bioinformatics and machine learning.

BACKGROUND: Obesity has emerged as a growing global public health concern over recent decades. Obesity prevalence exhibits substantial global variation, ranging from less than 5% in regions like China, Japan, and Africa to rates exceeding 75% in urban areas of Samoa.

Multi-Activity Step Counting Algorithm Using Deep Learning Foot Flat Detection with an IMU Inside the Sole of a Shoe.

Step counting devices were previously shown to be efficient in a variety of applications such as athletic training or patient's care programs. Various...

Prediction of Incident Diabetic Retinopathy in Adults With Type 1 Diabetes Using Machine Learning Approach: An Exploratory Study.

BACKGROUND: Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T...

Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

PURPOSE: Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited for patients with severe obesity and nonalcoholic...

Predicting non-responders to lifestyle intervention in prediabetes: a machine learning approach.

BACKGROUND: The clinical care process for people with prediabetes starts with lifestyle intervention, often escalating to more intense treatment due t...

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based an...

Non-invasive brain-machine interface control with artificial intelligence copilots.

Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the la...

Modeling health risks using neural network ensembles.

This study aims to demonstrate that demographics combined with biometrics can be used to predict obesity related chronic disease risk and produce a he...

The Bioprotective Effects of Marigold Tea Polyphenols on Obesity and Oxidative Stress Biomarkers in High-Fat-Sugar Diet-Fed Rats.

The research is aimed at exploring the potential of marigold petal tea (MPT), rich in polyphenol contents, against oxidative stress and obesity in a ...

Exploring key factors influencing depressive symptoms among middle-aged and elderly adult population: A machine learning-based method.

OBJECTIVE: This paper aims to investigate the key factors, including demographics, socioeconomics, physical well-being, lifestyle, daily activities an...

Development of the machine learning model that is highly validated and easily applicable to predict radiographic knee osteoarthritis progression.

Many models using the aid of artificial intelligence have been recently proposed to predict the progression of knee osteoarthritis. However, previous ...

Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset.

Patients with type 2 diabetes mellitus (T2DM) who have severe hypoglycemia (SH) poses a considerable risk of long-term death, especially among the eld...

Visualization obesity risk prediction system based on machine learning.

Obesity is closely associated with various chronic diseases.Therefore, accurate, reliable and cost-effective methods for preventing its occurrence and...

Explainable biology for improved therapies in precision medicine: AI is not enough.

Technological advances and high-throughput bio-chemical assays are rapidly changing ways how we formulate and test biological hypotheses, and how we t...

The revolution in high-throughput proteomics and AI.

The recent capability to measure thousands of plasma proteins from a tiny blood sample has provided a new dimension of expansive data that can advance...

Controlled and Real-Life Investigation of Optical Tracking Sensors in Smart Glasses for Monitoring Eating Behavior Using Deep Learning: Cross-Sectional Study.

BACKGROUND: The increasing prevalence of obesity necessitates innovative approaches to better understand this health crisis, particularly given its st...

Applying machine learning approaches for predicting obesity risk using US health administrative claims database.

INTRODUCTION: Body mass index (BMI) is inadequately recorded in US administrative claims databases. We aimed to validate the sensitivity and positive ...

Maternal dietary practices during pregnancy and obesity of neonates: a machine learning approach towards hierarchical and nested relationships in a Tibet Plateau cohort study.

Studies on obesity and risk factors from a life-course perspective among residents in the Tibet Plateau with recent economic growth and increasing obe...

Development of a Surgery-specific Comorbidity Score for Use in Administrative Data.

OBJECTIVE: To create a novel comorbidity score tailored for surgical database research. BACKGROUND: Despite their use in surgical research, the Elixha...