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Mitigating underreported error in food frequency questionnaire data using a supervised machine learning method and error adjustment algorithm

BACKGROUND: Food frequency questionnaires (FFQs) are one of the most useful tools for studying and understanding diet-disease relationships. However, because FFQs are self-reported data, they are susceptible to response bias, social desirability bias, and misclassification. Currently, several method...

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Detalles Bibliográficos
Autores principales:	Popoola, Anjolaoluwa Ayomide, Frediani, Jennifer Koren, Hartman, Terryl Johnson, Paynabar, Kamran
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2023
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492312/ https://www.ncbi.nlm.nih.gov/pubmed/37689645 http://dx.doi.org/10.1186/s12911-023-02262-9

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