An imbalance-aware deep neural network for early prediction of preeclampsia.

Bennett, Rachel; Mulla, Zuber D; Parikh, Pavan; Hauspurg, Alisse; Razzaghi, Talayeh

Bennett, Rachel; Mulla, Zuber D; Parikh, Pavan; Hauspurg, Alisse; Razzaghi, Talayeh.

Afiliação

Bennett R; School of Industrial and Systems Engineering, University of Oklahoma, Norman, Oklahoma, United States of America.
Mulla ZD; Department of Obstetrics and Gynecology, and Office of Faculty Development, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, Texas, United States of America.
Parikh P; Department of Public Health, Texas Tech University Health Sciences Center, Lubbock, Texas, United States of America.
Hauspurg A; Division of Maternal Fetal Medicine, University of Oklahoma Health Science Center, Oklahoma City, Oklahoma, United States of America.
Razzaghi T; Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.

PLoS One ; 17(4): e0266042, 2022.

Article em En | MEDLINE | ID: mdl-35385525

Assuntos

Pré-Eclâmpsia; Teorema de Bayes; Feminino; Humanos; Lactente; Redes Neurais de Computação; Pré-Eclâmpsia/diagnóstico; Pré-Eclâmpsia/prevenção & controle; Gravidez

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pré-Eclâmpsia Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pré-Eclâmpsia Idioma: En Ano de publicação: 2022 Tipo de documento: Article