AIMC Topic: Diet

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Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach.

Nutrition & diabetes May 27, 2022
BACKGROUND: Studies on Type-2 Diabetes Mellitus (T2DM) have revealed heterogeneous sub-populations in terms of underlying pathologies. However, the identification of sub-populations in epidemiological datasets remains unexplored. We here focus on the...
India Obesity Unsupervised Machine Learning Diet Diabetes Mellitus, Type 2 Humans
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Intake monitoring in free-living conditions: Overview and lessons we have learned.

Appetite May 26, 2022
The progress in artificial intelligence and machine learning algorithms over the past decade has enabled the development of new methods for the objective measurement of eating, including both the measurement of eating episodes as well as the measurem...
Diet Feeding Behavior Meals Artificial Intelligence Algorithms Humans
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Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology.

Nutrients Apr 20, 2022
Nutritional epidemiology employs observational data to discover associations between diet and disease risk. However, existing analytic methods of dietary data are often sub-optimal, with limited incorporation and analysis of the correlations between ...
Artificial Intelligence Machine Learning Models, Statistical Diet
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Natural language processing: A window to understanding skincare trends.

International journal of medical informatics Jan 24, 2022
BACKGROUND: Reddit is a popular social media discussion forum. Reddit data can be analyzed with natural language processing techniques to gain insights into public health questions by tracking frequency of discussion on relevant topics over time and ...
Humans Social Media Natural Language Processing Diet
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Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes.

Scientific reports Jan 7, 2022
Malnutrition is a multidomain problem affecting 54% of older adults in long-term care (LTC). Monitoring nutritional intake in LTC is laborious and subjective, limiting clinical inference capabilities. Recent advances in automatic image-based food est...
Diet Deep Learning Image Processing, Computer-Assisted Humans Reproducibility of Results Nutritive Value Early Diagnosis Automation Malnutrition Eating Predictive Value of Tests Nursing Homes Photography Long-Term Care Nutritional Status Meals
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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...
Meals Malnutrition Eating Feeding Behavior Image Processing, Computer-Assisted Inpatients Hospitalization Aged Artificial Intelligence Diet Energy Intake Humans Nutrition Assessment
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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...
Chromatography, High Pressure Liquid Diet Tandem Mass Spectrometry Mycotoxins Solid Phase Extraction
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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 ...
Food Labeling Nutrition Policy Algorithms Fast Foods Diet Automation Energy Intake Nutritional Status Humans Australia Nutrients Food Packaging Beverages Feeding Behavior Machine Learning Food Analysis Nutritive Value Dietary Fiber
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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...
Humans Artificial Intelligence Eating Diet Nutritional Status Mobile Applications
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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...
Artificial Intelligence Machine Learning Diet Eating Nutritional Status
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