Using Machine Learning to Predict Weight Gain in Adults: an Observational Analysis From the All of Us Research Program.

Journal: The Journal of surgical research
PMID: 39742657

Abstract

INTRODUCTION: Obesity, defined as a body mass index ≥30 kg/m, is a major public health concern in the United States. Preventative approaches are essential, but they are limited by an inability to accurately predict individuals at highest risk of weight gain. Our objective was to develop accurate weight gain prediction models using the National Institutes of Health All of Us dataset. We hypothesized that machine learning models using both electronic health record and behavioral survey data would outperform models using electronic health record data alone.

Authors

  • Dawda Jawara
    Department of Surgery, University of Wisconsin, Madison, Wisconsin.
  • Kate V Lauer
    Department of Surgery, University of Wisconsin, Madison, Wisconsin.
  • Manasa Venkatesh
    Department of Surgery, University of Wisconsin, Madison, Wisconsin.
  • Lily N Stalter
    Department of Surgery, University of Wisconsin, Madison, Wisconsin.
  • Bret Hanlon
    Statistics.
  • Matthew M Churpek
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.
  • Luke M Funk
    Department of Surgery, University of Wisconsin, Madison, Wisconsin; Department of Surgery, William S. Middleton Memorial VA, Madison, Wisconsin. Electronic address: funk@surgery.wisc.edu.

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