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Diet

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Artificial Intelligence-Based Diets: A Role in the Nutritional Treatment of Metabolic Dysfunction-Associated Steatotic Liver Disease?

Journal of human nutrition and dietetics : the official journal of the British Dietetic Association
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing global health concern. Effective management of this condition relies heavily on lifestyle modifications and dietary interventions. In this study, we sought to e...

Predicting dry matter intake in cattle at scale using gradient boosting regression techniques and Gaussian process boosting regression with Shapley additive explanation explainable artificial intelligence, MLflow, and its containerization.

Journal of animal science
Dry matter intake (DMI) is a measure critical to managing and evaluating livestock. Methods exist for quantifying individual DMI in dry lot settings that employ expensive intake systems. No methods exist to accurately measure individual DMI of grazin...

Challenges for Predictive Modeling With Neural Network Techniques Using Error-Prone Dietary Intake Data.

Statistics in medicine
Dietary intake data are routinely drawn upon to explore diet-health relationships, and inform clinical practice and public health. However, these data are almost always subject to measurement error, distorting true diet-health relationships. Beyond m...

Machine Learning Models Integrating Dietary Indicators Improve the Prediction of Progression from Prediabetes to Type 2 Diabetes Mellitus.

Nutrients
: Diet plays an important role in preventing and managing the progression from prediabetes to type 2 diabetes mellitus (T2DM). This study aims to develop prediction models incorporating specific dietary indicators and explore the performance in T2DM ...

Associations of dietary patterns with serum 25(OH) vitamin D and serum anemia related biomarkers among expectant mothers: A machine learning based approach.

International journal of medical informatics
BACKGROUND: Machine learning algorithms (MLA) gained prominence in nutritional epidemiology for analyzing dietary associations and uncovering intricate patterns within data. We explored dietary patterns associated with serum iron biomarkers and vitam...

Improving machine learning models through explainable AI for predicting the level of dietary diversity among Ethiopian preschool children.

Italian journal of pediatrics
BACKGROUND: Child nutrition in Ethiopia is a significant concern, particularly for preschool-aged children. Children must have a varied diet to ensure they receive all the essential nutrients for good health. Unfortunately, many children in Ethiopia ...

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

BMC medical informatics and decision making
Current research on the association between demographic variables and dietary patterns with atherosclerotic cardiovascular disease (ASCVD) is limited in breadth and depth. This study aimed to construct a machine learning (ML) algorithm that can accur...

Unique Microbial Characterisation of Oesophageal Squamous Cell Carcinoma Patients with Different Dietary Habits Based on Light Gradient Boosting Machine Learning Classifier.

Nutrients
: The microbiome plays an important role in cancer, but the relationship between dietary habits and the microbiota in oesophageal squamous cell carcinoma (ESCC) is not clear. The aim of this study is to explore the complex relationship between the mi...

The Influence of an AI-Driven Personalized Nutrition Program on the Human Gut Microbiome and Its Health Implications.

Nutrients
Personalized nutrition programs enhanced with artificial intelligence (AI)-based tools hold promising potential for the development of healthy and sustainable diets and for disease prevention. This study aimed to explore the impact of an AI-based pe...