ReviewObesity (Silver Spring, Md.)2024
Transforming Big Data into AI-ready data for nutrition and obesity research.
Review in Obesity (Silver Spring, Md.), 2024. 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 9 papers, 1 of them a synthesis 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on Applications of Artificial Intelligence for Obesity Prevention.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Data, Process, and Data-Driven Representations of Digital Twins in Diabetes: Scoping Review.JMIR diabetes · 2026Review
- Senescent obesity signature in breast cancer: a paradigm of reverse cardio-oncology.European heart journal · 2026Review
- AI-readiness Criteria for Biomedical Data.bioRxiv : the preprint server for biology · 2026Article
- Personalized Nutrition Through the Gut Microbiome in Metabolic Syndrome and Related Comorbidities.Nutrients · 2026Review
- Precision Prevention, Diagnostics, and Treatment of Obesity: Pipedream or Reality?Obesity (Silver Spring, Md.) · 2025Review
- Bridging the gap in obesity research: A consensus statement from the European Society for Clinical Investigation.European journal of clinical investigation · 2025Review
- Learning health system strategies in the AI era.npj health systems · 2025Review
- Succinic Acid Improves the Metabolism of High-Fat Diet-Induced Mice and Promotes White Adipose Browning.Nutrients · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
15 authors.
Funding
Abstract
objectiveBig Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)- and ML-ready data. These preprocessing steps are the most complex part of the entire modeling pipeline. Understanding the complexity of these steps by the end user is critical for reducing misunderstanding, faulty interpretation, and erroneous downstream conclusions.
methodsWe reviewed three popular obesity/nutrition Big Data sources: microbiome, metabolomics, and accelerometry. The preprocessing pipelines, specialized software, challenges, and how decisions impact final AI- and ML-ready products were detailed.
resultsOpportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented.
conclusionsBig Data have the exciting potential for identifying new modifiable factors that impact obesity research. However, to ensure accurate interpretation of conclusions arising from Big Data, the choices involved in preparing AI- and ML-ready data need to be transparent to investigators and clinicians relying on the conclusions.
Indexed as
Identifiers
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.