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

Internet

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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