Obesity

Differentiating adolescent suicidal and nonsuicidal self-harm with artificial intelligence: Beyond suicidal intent and capability for suicide.

Clinical differentiation between adolescent suicidal self-harm (SSH) and nonsuicidal self-harm (NSSH) is a significant challenge for mental health professionals, and its feasibility is controversial. The aim of the present study was to determine whether adolescent SSH can be differentiated from NSSH based on a holistic multimodel domain analysis (HMDA) of individual, psychological, sociodemographi...

Functionally characterizing obesity-susceptibility genes using CRISPR/Cas9, in vivo imaging and deep learning.

Hundreds of loci have been robustly associated with obesity-related traits, but functional characterization of candidate genes remains a bottleneck. Aiming to systematically characterize candidate genes for a role in accumulation of lipids in adipocytes and other cardiometabolic traits, we developed a pipeline using CRISPR/Cas9, non-invasive, semi-automated fluorescence imaging and deep learning-b...

Machine-learning approaches to predict individualized treatment effect using a randomized controlled trial.

Recent advancements in machine learning (ML) for analyzing heterogeneous treatment effects (HTE) are gaining prominence within the medical and epidemi...

Leveraging machine learning and rule extraction for enhanced transparency in emergency department length of stay prediction.

This study aims to address the critical issue of emergency department (ED) overcrowding, which negatively affects patient outcomes, wait times, and re...

Predicting Type 2 diabetes onset age using machine learning: A case study in KSA.

The rising prevalence of Type 2 Diabetes (T2D) in Saudi Arabia presents significant healthcare challenges. Estimating the age at onset of T2D can aid ...

Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients.

Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with...

Discovering Vitamin-D-Deficiency-Associated Factors in Korean Adults Using KNHANES Data Based on an Integrated Analysis of Machine Learning and Statistical Techniques.

: Vitamin D deficiency (VDD) is a global health concern associated with metabolic disease and immune dysfunction. Despite known risk factors like limi...

Factors associated with underweight, overweight, and obesity in Chinese children aged 3-14 years using ensemble learning algorithms.

BACKGROUND: Factors underlying the development of childhood underweight, overweight, and obesity are not fully understood. Traditional models have dra...

WDRIV-Net: a weighted ensemble transfer learning to improve automatic type stratification of lumbar intervertebral disc bulge, prolapse, and herniation.

The degeneration of the intervertebral discs in the lumbar spine is the common cause of neurological and physical dysfunctions and chronic disability ...

Transfer Learning Prediction of Early Exposures and Genetic Risk Score on Adult Obesity in Two Minority Cohorts.

Due to ethnic heterogeneity in genetic architecture, genetic risk score (GRS) constructed within the European population generally possesses poor port...

Estimation of Hematocrit Volume Using Blood Glucose Concentration through Extreme Gradient Boosting Regressor Machine Learning Model.

Lifestyle diseases such as cardiovascular disorders, diabetes, etc. affect the physiological metabolism and become chronic upon negligence. Diabetes i...

Machine Learning Assisted-Intelligent Lactic Acid Monitoring in Sweat Supported by a Perspiration-Driven Self-Powered Sensor.

Lactic acid has aroused increasing attention due to its close association with serious diseases. A real-time, dynamic, and intelligent detection metho...

Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study.

BACKGROUND: Modern lifestyle risk factors, like physical inactivity and poor nutrition, contribute to rising rates of obesity and chronic diseases lik...

Machine learning prediction of obesity-associated gut microbiota: identifying as a potential therapeutic target.

BACKGROUND: The rising prevalence of obesity and related metabolic disorders highlights the urgent need for innovative research approaches. Utilizing ...

Information Extraction from Clinical Texts with Generative Pre-trained Transformer Models.

Processing and analyzing clinical texts are challenging due to its unstructured nature. This study compared the performance of GPT (Generative Pre-tr...

Prediction of insulin resistance using multiple adaptive regression spline in Chinese women.

Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis assessment model is a straightforward and practical to...

Identification of hub biomarkers in liver post-metabolic and bariatric surgery using comprehensive machine learning (experimental studies).

BACKGROUND: The global prevalence of non-alcoholic fatty liver disease (NAFLD) is approximately 30%, and the condition can progress to non-alcoholic s...

Evaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learning.

Waist-to-hip ratio (WHR) is an essential predictor of cardiometabolic diseases, but traditional tape-based WHR measurements in children and adolescent...

Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction.

Heart failure is a leading cause of premature death, especially among individuals with a sedentary lifestyle. Early and accurate detection is essentia...

Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling.

BACKGROUND: Type 1 diabetes (T1D) is a chronic endocrine disorder characterized by high blood glucose levels, impacting millions of people globally. I...