ArticleAdvances in nutrition (Bethesda, Md.)2022
Machine Learning in Nutrition Research.
Article in Advances in nutrition (Bethesda, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 73 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
73 citing papers in PubMed, 3 syntheses or guidelines pooled it, 146 citations in OpenAlex.
- A Systematic Review of the Effect of Gene-Lifestyle Interactions on Metabolic-Disease-Related Traits in South Asian Populations.Nutrition reviews · 2025Pooled it
- Artificial Intelligence in Malnutrition: A Systematic Literature Review.Advances in nutrition (Bethesda, Md.) · 2024Pooled it
- Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review.Nutrients · 2024Pooled it
- Use of Machine Learning to Identify Determinants of Habitual-Preformed Water Intake.The Journal of nutrition · 2026Trial
- Global and regional inequalities in dairy recommendations: a natural language processing analysis of food-based dietary guidelines across income groups.Lancet regional health. Americas · 2026Article
- Exploring the relationship between social media use, eating behaviors, and depression in young women: a machine learning-based cross-sectional study.BMC public health · 2026Article
- Review
- Machine Learning and Artificial Intelligence in Nutrition Research: Analytical Methods, Applications, and Key Considerations.The Journal of nutrition · 2026Review
- Nutrient-Aware Personalized Meal Recommendation Using Structured Food Knowledge and Constraint Verification.Foods (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Guided Artificial Nutrition in Critical Illness: Integrating Indirect Calorimetry and BIVA for Metabolic Precision.Nutrients · 2026Review
- Principal Component-Based Comprehensive Physical Health Score Associates With Long-Term Death in Patients With Ischemic Stroke.Journal of the American Heart Association · 2026Article
- Computational Nutrition in Practice: Challenges and Opportunities From an Early-Career Perspective.The Journal of nutrition · 2026Article
- Smart Kiosk for Nutritional Management of People With Diabetes in Underserved Communities: Development and Technical Evaluation.JMIR formative research · 2026Article
- Explainable machine learning for depression risk prediction in adults with obesity: development of an online tool.BMC medical informatics and decision making · 2026Article
- Current Advancements in Untargeted Metabolomics Analysis and Testing Driven by Machine Learning: Prospects for Artificial Intelligence in Patient-Centric Healthcare Transformation.Applied biochemistry and biotechnology · 2026Review
- Machine learning prediction of food addiction in university students using demographic, anthropometric and personality traits.Scientific reports · 2026Article
- Association of Nontraditional Lipid Parameters With Long-Term Prognosis of Acute Ischemic Stroke: A Principal Components Analysis.Journal of the American Heart Association · 2026Article
- Tongue Characteristics and Musculoskeletal Pain in Patients with Type 2 Diabetes Mellitus: A Principal Component and Latent Profile Analysis.Journal of pain research · 2026Article
- An AI-driven evidence ecosystem for cardiometabolic nutrition: bridging the gap from multiomics to patient empowerment.Frontiers in nutrition · 2026Article
- Artificial intelligence-driven personalized dietary recommendations for gastric cancer high-risk populations: a narrative review.Frontiers in nutrition · 2026Review
13 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.