TY - JOUR
T1 - Transforming Big Data into AI-ready data for nutrition and obesity research
AU - Thomas, Diana M.
AU - Knight, Rob
AU - Gilbert, Jack A.
AU - Cornelis, Marilyn C.
AU - Gantz, Marie G.
AU - Burdekin, Kate
AU - Cummiskey, Kevin
AU - Sumner, Susan C.J.
AU - Pathmasiri, Wimal
AU - Sazonov, Edward
AU - Gabriel, Kelley Pettee
AU - Dooley, Erin E.
AU - Green, Mark A.
AU - Pfluger, Andrew
AU - Kleinberg, Samantha
N1 - Publisher Copyright:
© 2024 The Obesity Society.This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
PY - 2024/5
Y1 - 2024/5
N2 - 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.
AB - 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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U2 - 10.1002/oby.23989
DO - 10.1002/oby.23989
M3 - Review article
C2 - 38426232
AN - SCOPUS:85186187822
SN - 1930-7381
VL - 32
SP - 857
EP - 870
JO - Obesity
JF - Obesity
IS - 5
ER -