Use of a Doubly Robust Machine-Learning-Based Approach to Evaluate Body Mass Index as a Modifier of the Association Between Fruit and Vegetable Intake and Preeclampsia.

Bodnar, Lisa M; Cartus, Abigail R; Kennedy, Edward H; Kirkpatrick, Sharon I; Parisi, Sara M; Himes, Katherine P; Parker, Corette B; Grobman, William A; Simhan, Hyagriv N; Silver, Robert M; Wing, Deborah A; Perry, Samuel; Naimi, Ashley I

Bodnar, Lisa M; Cartus, Abigail R; Kennedy, Edward H; Kirkpatrick, Sharon I; Parisi, Sara M; Himes, Katherine P; Parker, Corette B; Grobman, William A; Simhan, Hyagriv N; Silver, Robert M; Wing, Deborah A; Perry, Samuel; Naimi, Ashley I.

Am J Epidemiol ; 191(8): 1396-1406, 2022 07 23.

Article en En | MEDLINE | ID: mdl-35355047

Asunto(s)

Frutas; Preeclampsia; Índice de Masa Corporal; Estudios de Cohortes; Dieta; Femenino; Humanos; Aprendizaje Automático; Preeclampsia/epidemiología; Embarazo; Verduras

Palabras clave

birth; body mass index; dietary patterns; machine learning; obesity; preeclampsia; pregnancy; pregnant women

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Preeclampsia / Frutas Tipo de estudio: Etiology_studies / Incidence_studies / Observational_studies / Qualitative_research / Risk_factors_studies Límite: Female / Humans / Pregnancy Idioma: En Revista: Am J Epidemiol Año: 2022 Tipo del documento: Article

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