Machine-learning-based prediction of pre-eclampsia using first-trimester maternal characteristics and biomarkers.

Ansbacher-Feldman, Z; Syngelaki, A; Meiri, H; Cirkin, R; Nicolaides, K H; Louzoun, Y

Ansbacher-Feldman, Z; Syngelaki, A; Meiri, H; Cirkin, R; Nicolaides, K H; Louzoun, Y.

Afiliación

Ansbacher-Feldman Z; Department of Mathematics, Bar Ilan University, Ramat Gan, Israel.
Syngelaki A; Fetal Medicine Research Institute, King's College Hospital, London, UK.
Meiri H; The ASPRE Consortium and TeleMarpe, Tel Aviv, Israel.
Cirkin R; Department of Mathematics, Bar Ilan University, Ramat Gan, Israel.
Nicolaides KH; Fetal Medicine Research Institute, King's College Hospital, London, UK.
Louzoun Y; Department of Mathematics, Bar Ilan University, Ramat Gan, Israel.

Ultrasound Obstet Gynecol ; 60(6): 739-745, 2022 12.

Article en En | MEDLINE | ID: mdl-36454636

Asunto(s)

Preeclampsia; Embarazo; Recién Nacido; Femenino; Humanos; Primer Trimestre del Embarazo; Preeclampsia/diagnóstico; Factor de Crecimiento Placentario; Estudios Prospectivos; Aprendizaje Automático; Biomarcadores; Aspirina

Palabras clave

artificial intelligence; first trimester; machine learning; mean arterial pressure; neural network; placental growth factor; posterior risk; pre-eclampsia; prior risk; uterine artery Doppler

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Preeclampsia Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Ultrasound Obstet Gynecol Asunto de la revista: DIAGNOSTICO POR IMAGEM / GINECOLOGIA / OBSTETRICIA Año: 2022 Tipo del documento: Article

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