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Diet

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Diet Quality and Caloric Accuracy in AI-Generated Diet Plans: A Comparative Study Across Chatbots.

Nutrients
With the rise of artificial intelligence (AI) in nutrition and healthcare, AI-driven chatbots are increasingly recognised as potential tools for generating personalised diet plans. This study aimed to evaluate the capabilities of three popular chatb...

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.

Journal of dairy science
As the call for an international standard for milk from grassland-based production systems continues to grow, so too do the monitoring and evaluation policies surrounding this topic. Individual stipulations by countries and milk producers to market t...

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

The British journal of nutrition
Studies on obesity and risk factors from a life-course perspective among residents in the Tibet Plateau with recent economic growth and increasing obesity are important and urgently needed. The birth cohort in this area provides a unique opportunity ...

Enhancing dietary analysis: Using machine learning for food caloric and health risk assessment.

Journal of food science
In the wake of growing concerns regarding diet-related health issues, this study investigates the application of machine learning methods to estimate the energy content and classify the health risks of foods based on the USDA National Nutrient Databa...

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

Annals of vascular surgery
BACKGROUND: Peripheral arterial disease (PAD) is a common manifestation of atherosclerosis, affecting over 200 million people worldwide. The incidence of PAD is increasing due to the aging population. Common risk factors include smoking, diabetes, an...

Predicting metabolite response to dietary intervention using deep learning.

Nature communications
Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living ...

Investigation and Assessment of AI's Role in Nutrition-An Updated Narrative Review of the Evidence.

Nutrients
BACKGROUND: Artificial Intelligence (AI) technologies are now essential as the agenda of nutrition research expands its scope to look at the intricate connection between food and health in both an individual and a community context. AI also helps in ...

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

Journal of health, population, and nutrition
BACKGROUND: Diabetes mellitus, an endocrine system disease, is a common disease involving many patients worldwide. Many studies are performed to evaluate the correlation between micronutrients/macronutrients on diabetes but few of them have a high st...