Prediction of vaginal birth after cesarean deliveries using machine learning

Efforts to reduce cesarean delivery rates to 12–15% have been undertaken worldwide. Special focus has been directed towards parturients who undergo a trial of labor after cesarean delivery to reduce the burden of repeated cesarean deliveries. Complication rates are lowest when a vaginal birth is ach...

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Veröffentlicht in:	American journal of obstetrics and gynecology 2020-06, Vol.222 (6), p.613.e1-613.e12
Hauptverfasser:	Lipschuetz, Michal, Guedalia, Joshua, Rottenstreich, Amihai, Novoselsky Persky, Michal, Cohen, Sarah M., Kabiri, Doron, Levin, Gabriel, Yagel, Simcha, Unger, Ron, Sompolinsky, Yishai
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Apgar Score Area Under Curve Cesarean Section - statistics & numerical data Delivery, Obstetric Extraction, Obstetrical - statistics & numerical data Feasibility Studies Female Fetal Weight Gestational Age Head - anatomy & histology Humans Infant, Newborn Machine Learning Organ Size Parity personalized prediction Pregnancy Retrospective Studies Risk Assessment Risk Factors ROC Curve Tertiary Care Centers Trial of Labor Uterine Rupture - epidemiology Vaginal Birth after Cesarean - statistics & numerical data vaginal birth after cesarean delivery
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