Primary Care

Diet & Nutrition

Latest AI and machine learning research in diet & nutrition for healthcare professionals.

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Advanced deep learning models for predicting elemental concentrations in iron ore mine using XRF data: a cost-effective alternative to ICP-MS methods.

Accurate elemental analysis is a critical requirement for mineral exploration, particularly in regio...

An interpretable machine learning model with demographic variables and dietary patterns for ASCVD identification: from U.S. NHANES 1999-2018.

Current research on the association between demographic variables and dietary patterns with atherosc...

A new approach to dilution prediction of underground mine gold using computing techniques.

Controlling ore dilution in underground mining is challenging. In this study, data from a Brazilian ...

A Scoping Review of Artificial Intelligence for Precision Nutrition.

With the role of artificial intelligence (AI) in precision nutrition rapidly expanding, a scoping re...

Machine learning approach on plasma proteomics identifies signatures associated with obesity in the KORA FF4 cohort.

AIMS: This study investigated the role of plasma proteins in obesity to identify predictive biomarke...

Automatic analysis of high, medium, and low activities of broilers with heat stress operations via image processing and machine learning.

Heat stress is a major welfare problem in the poultry industry, altering broilers' activity levels. ...

AI-Powered Analysis of Weight Loss Reports from Reddit: Unlocking Social Media's Potential in Dietary Assessment.

: The increasing use of social media for sharing health and diet experiences presents new opportunit...

Utilization of tree-based machine learning models for predicting low birth weight cases.

BACKGROUND: Low birth weight (LBW) is a health condition that affects over 20 million gestational ou...

Is personality associated with the lived experience of the NHS England low calorie diet programme: A pilot study.

This pilot study explored the use of a novel behavioural artificial intelligence (AI) tool to examin...

Artificial intelligence for osteoporosis detection on panoramic radiography: A systematic review and meta analysis.

INTRODUCTION: Osteoporosis is a disease characterized by low bone mineral density and an increased r...

Cognitive performance classification of older patients using machine learning and electronic medical records.

Dementia rates are projected to increase significantly by 2050, posing considerable challenges for h...

Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model.

Exposure to three primary xenoestrogens (XEs), including phthalates, parabens, and phenols, has been...

Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia.

BACKGROUND: The accuracy of current tools for predicting adverse events in older inpatients with pos...

The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations.

Greater than 650 million individuals worldwide are categorized as obese, which is associated with si...

Using visible and NIR hyperspectral imaging and machine learning for nondestructive detection of nutrient contents in sorghum.

Nondestructive, rapid, and accurate detection of nutritional compositions in sorghum is crucial for ...

Hybrid Greylag Goose deep learning with layered sparse network for women nutrition recommendation during menstrual cycle.

A complex biological process involves physical changes and hormonal fluctuation in the menstrual cyc...

Leveraging OGTT derived metabolic features to detect Binge-eating disorder in individuals with high weight: a "seek out" machine learning approach.

Binge eating disorder (BED) carries a 6 times higher risk for obesity and accounts for roughly 30% o...

Machine learning modeling for predicting adherence to physical activity guideline.

This study aims to create predictive models for PA guidelines by using ML and examine the critical d...

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 character...

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