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 em En | MEDLINE | ID: mdl-35355047

Assuntos

Frutas; Pré-Eclâmpsia; Índice de Massa Corporal; Estudos de Coortes; Dieta; Feminino; Humanos; Aprendizado de Máquina; Pré-Eclâmpsia/epidemiologia; Gravidez; Verduras

Palavras-chave

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

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pré-Eclâmpsia / Frutas Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Qualitative_research / Risk_factors_studies Limite: Female / Humans / Pregnancy Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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