Prediction of hypertension and diabetes in twin pregnancy using machine learning model based on characteristics at first prenatal visit: national registry study.

Journal: Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology

PMID: 38805609

Abstract

OBJECTIVE: To develop a prediction model for hypertensive disorders of pregnancy (HDP) and gestational diabetes mellitus (GDM) in twin pregnancy using characteristics obtained at the first prenatal visit.

Authors

H J Mustafa

Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
E Kalafat

Department of Obstetrics and Gynecology, Koc University School of Medicine, Istanbul, Turkey.
S Prasad

School of Electrical Engineering and Computer Science, Penn State, University Park, PA 16802 United States of America.
M-H Heydari

Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
R N Nunge

Indiana University School of Medicine, Indianapolis, IN, USA.
A Khalil

Department of Radiology, Hôpital Bichat, Assistance Publique-Hopitaux de Paris, 75018 Paris, France.

Keywords

Adult Cross-Sectional Studies Diabetes, Gestational Female Humans Hypertension, Pregnancy-Induced Incidence Machine Learning Predictive Value of Tests Pregnancy Pregnancy, Twin Prenatal Care Registries Risk Factors United States

External Resources

View on PubMed Access via DOI PubMed (38805609)

Prediction of hypertension and diabetes in twin pregnancy using machine learning model based on characteristics at first prenatal visit: national registry study.

Abstract

Authors

Keywords

External Resources

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