Prediction of 24-Hour Urinary Sodium Excretion Using Machine-Learning Algorithms.

Journal: Journal of the American Heart Association
PMID: 38726910

Abstract

BACKGROUND: Accurate quantification of sodium intake based on self-reported dietary assessments has been a persistent challenge. We aimed to apply machine-learning (ML) algorithms to predict 24-hour urinary sodium excretion from self-reported questionnaire information.

Authors

  • Rikuta Hamaya
    Division of Cardiovascular Medicine, Tsuchiura Kyodo General Hospital, Ibaraki, Japan.
  • Molin Wang
    Department of Epidemiology Harvard T. H. Chan School of Public Health Boston MA USA.
  • Stephen P Juraschek
    Department of Medicine, Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA.
  • Kenneth J Mukamal
    Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
  • JoAnn E Manson
    Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Deirdre K Tobias
    Division of Preventive Medicine, Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA.
  • Qi Sun
    Department of Orthopedics, Shanghai Tenth People's Hospital, Tongji University, School of Medicine, Shanghai, 200072, P.R.China.
  • Gary C Curhan
    Epidemiology, and.
  • Walter C Willett
    Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA.
  • Eric B Rimm
    Department of Epidemiology Harvard T. H. Chan School of Public Health Boston MA USA.
  • Nancy R Cook
    Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.

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