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


Thomas, Diana M ORCID: 0000-0003-2641-9304, Knight, Rob, Gilbert, Jack A, Cornelis, Marilyn C, Gantz, Marie G, Burdekin, Kate, Cummiskey, Kevin, Sumner, Susan CJ, Pathmasiri, Wimal, Sazonov, Edward et al (show 5 more authors) (2024) Transforming Big Data into AI-ready data for nutrition and obesity research. Obesity (Silver Spring, Md.), 32 (5). pp. 857-870.

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Abstract

<h4>Objective</h4>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.<h4>Methods</h4>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.<h4>Results</h4>Opportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented.<h4>Conclusions</h4>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.

Item Type: Article
Uncontrolled Keywords: Nutrition, Obesity, Prevention, Bioengineering, Networking and Information Technology R&D (NITRD)
Divisions: Faculty of Science and Engineering > School of Environmental Sciences
Depositing User: Symplectic Admin
Date Deposited: 05 Jan 2024 16:22
Last Modified: 26 Apr 2024 16:54
DOI: 10.1002/oby.23989
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3177707
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