Primary Care

Diet & Nutrition

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

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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...

Artificial intelligence for opportunistic osteoporosis screening with a Hounsfield Unit in chronic obstructive pulmonary disease patients.

INTRODUCTION: To investigate the accuracy of an artificial intelligence (AI) prototype in determinin...

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 chall...

Machine learning prediction of glaucoma by heavy metal exposure: results from the National Health and Nutrition Examination Survey 2005 to 2008.

Using follow-up data from the National Health and Nutrition Examination Survey (NHANES) database, we...

Biopsychosocial based machine learning models predict patient improvement after total knee arthroplasty.

Total knee arthroplasty (TKA) is an effective treatment for end stage osteoarthritis. However, biops...

Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes.

BACKGROUND: Diabetes mellitus, an endocrine system disease, is a common disease involving many patie...

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...

Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset.

Asthma diagnosis poses challenges due to underreporting of symptoms, misdiagnoses, and limitations i...

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...

Autoregressive exogenous neural structures for synthetic datasets of olive disease control model with fractional Grünwald-Letnikov solver.

A fundamental element of the Mediterranean diet, olive oil is abundant in heart-healthy monounsatura...

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 t...

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 n...

Deep learning opportunistic screening for osteoporosis and osteopenia using radiographs of the foot or ankle - A pilot study.

BACKGROUND: The gold standard method for diagnosing low bone mineral density (BMD) is using dual-ene...

Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning.

In the wagyu industry worldwide, high-quality marbling beef is produced by promoting intramuscular f...

Deep learning-assisted Raman spectroscopy for automated identification of specific minerals.

Raman spectroscopy is applied as an important method for material identification in field geology. H...

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...

Machine Learning Analysis of Nutrient Associations with Peripheral Arterial Disease: Insights from NHANES 1999-2004.

BACKGROUND: Peripheral arterial disease (PAD) is a common manifestation of atherosclerosis, affectin...

Machine learning models for water safety enhancement.

Humans encounter both natural and artificial radiation sources, including cosmic rays, primordial ra...

Risk factor assessment of prediabetes and diabetes based on epidemic characteristics in new urban areas: a retrospective and a machine learning study.

To explore in depth the characteristics of the risk factors for diabetes and prediabetes pathogenesi...

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