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

Journal: Obesity (Silver Spring, Md.)
PMID: 38426232

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.

Authors

  • Diana M Thomas
    United States Military Academy, West Point, New York, United States of America.
  • Rob Knight
    Department of Pediatrics, University of California, San Diego School of Medicine, La Jolla, CA 92093, USA; Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA 92093, USA.
  • Jack A Gilbert
    Department of Pediatrics and Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.
  • Marilyn C Cornelis
    Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
  • Marie G Gantz
    Biostatics and Epidemiology Division, Research Triangle Institute International, Research Triangle Park, North Carolina, USA.
  • Kate Burdekin
    Biostatics and Epidemiology Division, Research Triangle Institute International, Research Triangle Park, North Carolina, USA.
  • Kevin Cummiskey
    Department of Mathematical Sciences, United States Military Academy, West Point, New York, USA.
  • Susan C J Sumner
    Department of Nutrition, Nutrition Research Institute, University of North Carolina Chapel Hill, Kannapolis, North Carolina, USA.
  • Wimal Pathmasiri
    Department of Nutrition, Nutrition Research Institute, University of North Carolina Chapel Hill, Kannapolis, North Carolina, USA.
  • Edward Sazonov
  • Kelley Pettee Gabriel
    Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Erin E Dooley
    Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Mark A Green
    Geographic Data Science Lab, Department of Geography & Planning, University of Liverpool, Liverpool, United Kingdom.
  • Andrew Pfluger
    Department of Geography and Environmental Engineering, United States Military Academy, West Point, New York, USA.
  • Samantha Kleinberg
    Department of Computer Science, Stevens Institute of Technology, Hoboken, NJ, 07030, USA.

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