A machine learning model to predict spontaneous vaginal delivery failure for term nulliparous women: An observational study.

Journal: International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics

PMID: 38899565

Abstract

OBJECTIVE: This study aims to construct and evaluate a model to predict spontaneous vaginal delivery (SVD) failure in term nulliparous women based on machine learning algorithms.

Authors

Jing Xu

First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
Zifeng Liu

Shandong Yankuang Group Changlong Cable Manufacturing Co., Ltd, Jining, China.
Yaxin Lu

Department of Big Data and Artificial Intelligence, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.
Zhijuan Zheng

Department of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.
Xinling Zhang

Keywords

Adult Cervical Length Measurement Delivery, Obstetric Female Fetal Weight Humans Machine Learning Natural Childbirth Parity Predictive Value of Tests Pregnancy Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (38899565)

A machine learning model to predict spontaneous vaginal delivery failure for term nulliparous women: An observational study.

Abstract

Authors

Keywords

External Resources

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