AIMC Topic: Diet
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Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients.
Nutrients
Dec 17, 2021
Malnutrition is common, especially among older, hospitalised patients, and is associated with higher mortality, longer hospitalisation stays, infections, and loss of muscle mass. It is therefore of utmost importance to employ a proper method for diet...
Rapid and sensitive UHPLC-MS/MS methods for dietary sample analysis of 43 mycotoxins in China total diet study.
Journal of advanced research
Oct 19, 2021
INTRODUCTION: Mycotoxins are toxic metabolites produced by fungi that commonly contaminate foods. As recommended by the World Health Organization, total diet study (TDS) is the most efficient and effective way to estimate the dietary intakes of certa...
An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods.
Nutrients
Sep 14, 2021
Underconsumption of dietary fiber is prevalent worldwide and is associated with multiple adverse health conditions. Despite the importance of fiber, the labeling of fiber content on packaged foods and beverages is voluntary in most countries, making ...
A Review of Digital Innovations for Diet Monitoring and Precision Nutrition.
Journal of diabetes science and technology
Sep 1, 2021
This article provides an up-to-date review of technological advances in 3 key areas related to diet monitoring and precision nutrition. First, we review developments in mobile applications, with a focus on food photography and artificial intelligence...
Applicability of machine learning techniques in food intake assessment: A systematic review.
Critical reviews in food science and nutrition
Jul 29, 2021
The evaluation of food intake is important in scientific research and clinical practice to understand the relationship between diet and health conditions of an individual or a population. Large volumes of data are generated daily in the health sector...
Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA.
Scientific reports
Mar 26, 2021
Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributio...
The potential of artificial intelligence in enhancing adult weight loss: a scoping review.
Public health nutrition
Feb 17, 2021
OBJECTIVE: To present an overview of how artificial intelligence (AI) could be used to regulate eating and dietary behaviours, exercise behaviours and weight loss.
Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS.
Circulation. Heart failure
Feb 9, 2021
BACKGROUND: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment...
Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques.
Scientific reports
Jan 21, 2021
The increased prevalence of childhood obesity is expected to translate in the near future into a concomitant soaring of multiple cardio-metabolic diseases. Obesity has a complex, multifactorial etiology, that includes multiple and multidomain potenti...
Optimising an FFQ Using a Machine Learning Pipeline to teach an Efficient Nutrient Intake Predictive Model.
Nutrients
Dec 10, 2020
Food frequency questionnaires (FFQs) are the most commonly selected tools in nutrition monitoring, as they are inexpensive, easily implemented and provide useful information regarding dietary intake. They are usually carefully drafted by experts from...
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