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Review
. 2024 May;32(5):857-870.
doi: 10.1002/oby.23989. Epub 2024 Mar 1.

Transforming Big Data into AI-ready data for nutrition and obesity research

Diana M Thomas  1 , Rob Knight  2 , Jack A Gilbert  3 , Marilyn C Cornelis  4 , Marie G Gantz  5 , Kate Burdekin  5 , Kevin Cummiskey  1 , Susan C J Sumner  6 , Wimal Pathmasiri  6 , Edward Sazonov  7 , Kelley Pettee Gabriel  8 , Erin E Dooley  8 , Mark A Green  9 , Andrew Pfluger  10 , Samantha Kleinberg  11
Affiliations
Review

Transforming Big Data into AI-ready data for nutrition and obesity research

Diana M Thomas et al. Obesity (Silver Spring). 2024 May.

Abstract

Objective: Big 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.

Methods: We 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.

Results: Opportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented.

Conclusions: Big 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.

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Conflict of interest statement

Conflicts of Interest: The authors declare no conflicts of interest.

Figures

Figure 1:
Figure 1:
Pipeline flow chart depicting the transformation from raw microbiome data to data that is AI ready.
Figure 2a:
Figure 2a:
Pipeline flow chart depicting the transformation from raw mass spectrometry metabolomics data to data that is AI ready (EIC= extracted ion chromatogram).
Figure 2b:
Figure 2b:
Pipeline flow chart depicting the transformation from NMR spectroscopy metabolomics data to data that is AI ready. Data matrix can be prepared using binned data or annotated/ library matched data. For binned data approach, library matching/ database search is performed on select important NMR bins.
Figure 3
Figure 3
Pipeline flow chart depicting the transformation from raw accelerometry data to data that is AI ready for physical activity analysis.
See this image and copyright information in PMC

References

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