Diet & Nutrition

Effective Removal of Selenium from Aqueous Solution using Iron-modified Dolochar: A Comprehensive Study and Machine Learning Predictive Analysis.

Selenium (Se) is an essential micronutrient for human beings, but excess concentration can lead to m...

Predicting structure-targeted food bioactive compounds for middle-aged and elderly Asians with myocardial infarction: insights from genetic variations and bioinformatics-integrated deep learning analysis.

Myocardial infarction (MI) is a significant global health issue. Despite the advances in genome-wide...

Development and validation of machine-learning models of diet management for hyperphenylalaninemia: a multicenter retrospective study.

BACKGROUND: Assessing dietary phenylalanine (Phe) tolerance is crucial for managing hyperphenylalani...

Association of N-terminal pro-B natriuretic peptide with all-cause mortality and cardiovascular mortality in obese and non-obese populations and the development of a machine learning prediction model: National Health and Nutrition Examination Survey (NHANES) 1999-2004.

AIMS: To explore the potential of N-terminal pro-B natriuretic peptide (NTproBNP) in identifying adv...

Predictive modeling of lean body mass, appendicular lean mass, and appendicular skeletal muscle mass using machine learning techniques: A comprehensive analysis utilizing NHANES data and the Look AHEAD study.

This study addresses the pressing need for improved methods to predict lean mass in adults, and in p...

Metabolic syndrome predictive modelling in Bangladesh applying machine learning approach.

Metabolic syndrome (MetS) is a cluster of interconnected metabolic risk factors, including abdominal...

Artificial intelligence assisted automatic screening of opportunistic osteoporosis in computed tomography images from different scanners.

OBJECTIVES: It is feasible to evaluate bone mineral density (BMD) and detect osteoporosis through an...

Deep Learning-Based Obesity Identification System for Young Adults Using Smartphone Inertial Measurements.

Obesity recognition in adolescents is a growing concern. This study presents a deep learning-based o...

The Effectiveness of Artificial Intelligence-based Pedicle Screw Trajectory Planning in Patients With Different Levels of Bone Mineral Density.

STUDY DESIGN: Retrospective cohort study.

Predictive modeling of rice milling degree for three typical Chinese rice varieties using interpretative machine learning methods.

Brown rice over-milling causes high economic and nutrient loss. The rice degree of milling (DOM) det...

Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at the herd level using mid-infrared spectral information from routine milk control.

As the call for an international standard for milk from grassland-based production systems continues...

Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.

PURPOSE: This study aimed to develop machine learning methods to estimate bone mineral density and d...

Obesity prediction: Novel machine learning insights into waist circumference accuracy.

AIMS: This study aims to enhance the precision of obesity risk assessments by improving the accuracy...

Evaluation of online chat-based artificial intelligence responses about inflammatory bowel disease and diet: correspondence.

Artificial Intelligence-Based Classification of CT Images Using a Hybrid SpinalZFNet.

The kidney is an abdominal organ in the human body that supports filtering excess water and waste fr...

Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study.

Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, wi...

Development and internal validation of an artificial intelligence-assisted bowel sounds auscultation system to predict early enteral nutrition-associated diarrhoea in acute pancreatitis: a prospective observational study.

An artificial intelligence-assisted prediction model for enteral nutrition-associated diarrhoea (EN...

Advancing geospatial preconception health research in primary care through medical informatics and artificial intelligence.

Established life course approaches suggest that health status in adulthood can be influenced by even...

Preventive machine learning models incorporating health checkup data and hair mineral analysis for low bone mass identification.

Machine learning (ML) models have been increasingly employed to predict osteoporosis. However, the i...

Predictability of varicocele repair success: preliminary results of a machine learning-based approach.

Varicocele is a prevalent condition in the infertile male population. However, to date, which patien...

Infrared spectroscopy coupled with machine learning algorithms for predicting the detailed milk mineral profile in dairy cattle.

Milk minerals are not only essential components for human health, but they can be informative for mi...