Prediction of 24‐Hour Urinary Sodium Excretion Using Machine‐Learning Algorithms

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. Methods and Results We analyzed...

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Autores principales:	Rikuta Hamaya, Molin Wang, Stephen P. Juraschek, Kenneth J. Mukamal, JoAnn E. Manson, Deirdre K. Tobias, Qi Sun, Gary C. Curhan, Walter C. Willett, Eric B. Rimm, Nancy R. Cook
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Wiley 2024-05-01
Colección:	Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
Materias:	dietary sodium food frequency questionnaire hypertension machine learning measurement error
Acceso en línea:	https://www.ahajournals.org/doi/10.1161/JAHA.123.034310
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