Identifying Young Adults at High Risk for Weight Gain Using Machine Learning.

Journal: The Journal of surgical research
PMID: 37329635

Abstract

INTRODUCTION: Weight gain among young adults continues to increase. Identifying adults at high risk for weight gain and intervening before they gain weight could have a major public health impact. Our objective was to develop and test electronic health record-based machine learning models to predict weight gain in young adults with overweight/class 1 obesity.

Authors

  • Jacqueline A Murtha
    Department of Surgery, University of Wisconsin, Madison, Wisconsin.
  • Jen Birstler
    Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin.
  • Lily Stalter
    Department of Surgery, University of Wisconsin, Madison, Wisconsin.
  • Dawda Jawara
    Department of Surgery, University of Wisconsin, Madison, Wisconsin.
  • Bret M Hanlon
    Department of Surgery, University of Wisconsin, Madison, Wisconsin; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin.
  • Lawrence P Hanrahan
    Department of Family Medicine and Community Health, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
  • 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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