Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared with traditional methods

Hu, Guanlan; Ahmed, Mavra; L’Abbé, Mary R.

doi:10.1016/j.ajcnut.2022.11.022

Cited by 19 publications

(10 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research efforts outlined in [ 21 ] align with a growing demand for high-quality and internationally comparable statistics to promote objective metrics, reproducibility, and data-driven decision-making, advancing our convergence towards the Sustainable Development Goals (SDGs) [ 28 , 29 ]. Artificial intelligence (AI) methodologies [ 30 ], in particular, are now welcomed as objective data-driven tools to advance populations’ nutrition security, a concept underpinning SDGs ‘zero-hunger’, ‘good health and well-being’, ‘industry, innovation, and infrastructure’, and ‘reduce inequalities’.…”

Section: Introductionmentioning

confidence: 99%

GroceryDB: Prevalence of Processed Food in Grocery Stores

Ravandi

Mehler

Barabási

et al. 2022

Preprint

View full text Add to dashboard Cite

The offering of grocery stores is a strong driver of consumer decisions, shaping their diet and long-term health. While processed food has been increasingly associated with unhealthy diet, information on the degree of processing characterising an item in a store is virtually impossible to obtain, limiting the ability of individuals to make informed choices. Here we introduce GroceryDB, a database with over 50,000 food items sold by Walmart, Target, and Wholefoods, unveiling the degree of processing characterizing each food. GroceryDB indicates that 73% of the US food supply is ultra-processed, and on average ultra-processed foods are 52% cheaper than minimally-processed alternatives. We find that the nutritional choices of the consumers, translated as the degree of food processing, strongly depend on the food categories and grocery stores. We show that there is no single nutrient or ingredient “bio-marker” for ultra-processed food, allowing us to quantify the individual contribution of over 1,000 ingredients to ultra-processing. GroceryDB and the associated http://TrueFood.Tech/ website make this information available, aiming to simultaneously empower the consumers to make healthy food choices, and aid policy makers to reform the food supply.

show abstract

Section: Introductionmentioning

confidence: 99%

GroceryDB: Prevalence of Processed Food in Grocery Stores

Ravandi

Mehler

Barabási

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Digitalization and technology have been increasingly used in healthcare, including nutritional interventions. This has greatly facilitated individualization by using algorithms and databases to customize nutritional care plans of individuals [39,40], leading to a more efficient and less complex process. Further research is needed to address these questions and inform the design of future interventions.…”

Section: Discussionmentioning

confidence: 99%

A Conceptual Study on Characterizing the Complexity of Nutritional Interventions for Malnourished Older Adults in Hospital Settings: An Umbrella Review Approach

Wong,

Huang,

Banks

et al. 2024

Healthcare

View full text Add to dashboard Cite

Introduction: Malnutrition is a widespread and intricate issue among hospitalized adults, necessitating a wide variety of nutritional strategies to address its root causes and repercussions. The primary objective of this study is to systematically categorize nutritional interventions into simple or complex, based on their resource allocation, strategies employed, and predictors of intervention complexity in the context of adult malnutrition in hospital settings. Methods: A conceptual evaluation of 100 nutritional intervention studies for adult malnutrition was conducted based on data from a recent umbrella review (patient population of mean age > 60 years). The complexity of interventions was categorized using the Medical Research Council 2021 Framework for Complex Interventions. A logistic regression analysis was employed to recognize variables predicting the complexity of interventions. Results: Interventions were divided into three principal categories: education and training (ET), exogenous nutrient provision (EN), and environment and services (ES). Most interventions (66%) addressed two or more of these areas. A majority of interventions were delivered in a hospital (n = 75) or a hospital-to-community setting (n = 25), with 64 studies being classified as complex interventions. The logistic regression analysis revealed three variables associated with intervention complexity: the number of strategies utilized, the targeted areas, and the involvement of healthcare professionals. Complex interventions were more likely to be tailored to individual needs and engage multiple healthcare providers. Conclusions: The study underlines the importance of considering intervention complexity in addressing adult malnutrition. Findings advocate for a comprehensive approach to characterizing and evaluating nutritional interventions in future research. Subsequent investigations should explore optimal balances between intervention complexity and resource allocation, and assess the effectiveness of complex interventions across various settings, while considering novel approaches like telehealth.

show abstract

“…The same system was validated in Vietnam [18 ▪ ]. Another group focused on food labels, demonstrating that natural language processing models could accurately determine nutrient contents of food from images of their packaging [19]. Whilst still in their infancy, these approaches to food intake monitoring show promise in allowing large-scale data gathering using modern technology to reduce the time and costs associated with traditional food intake interviews.…”

Section: Population-level Studies Of Disease Riskmentioning

confidence: 99%

The use of machine learning in paediatric nutrition

Young,

Johnson,

Beattie

2024

Current Opinion in Clinical Nutrition &Amp; Metabolic Care

View full text Add to dashboard Cite

Purpose of review In recent years, there has been a burgeoning interest in using machine learning methods. This has been accompanied by an expansion in the availability and ease of use of machine learning tools and an increase in the number of large, complex datasets which are suited to machine learning approaches. This review summarizes recent work in the field and sets expectations for its impact in the future. Recent findings Much work has focused on establishing good practices and ethical frameworks to guide the use of machine learning in research. Machine learning has an established role in identifying features in ‘omics’ research and is emerging as a tool to generate predictive models to identify people at risk of disease and patients at risk of complications. They have been used to identify risks for malnutrition and obesity. Machine learning techniques have also been used to develop smartphone apps to track behaviour and provide healthcare advice. Summary Machine learning techniques are reaching maturity and their impact on observational data analysis and behaviour change will come to fruition in the next 5 years. A set of standards and best practices are emerging and should be implemented by researchers and publishers.

show abstract

Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared with traditional methods

Cited by 19 publications

References 29 publications

GroceryDB: Prevalence of Processed Food in Grocery Stores

GroceryDB: Prevalence of Processed Food in Grocery Stores

A Conceptual Study on Characterizing the Complexity of Nutritional Interventions for Malnourished Older Adults in Hospital Settings: An Umbrella Review Approach

The use of machine learning in paediatric nutrition

Contact Info

Product

Resources

About