Predictive efficacy of machine-learning algorithms on intrahepatic cholestasis of pregnancy based on clinical and laboratory indicators.

Journal: The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians

Published Date: Dec 3, 2024

Abstract

OBJECTIVES: Intrahepatic cholestasis of pregnancy (ICP), a condition exclusive to pregnancy, necessitates prompt identification and intervention to improve the perinatal outcomes. This study aims to develop suitable machine-learning models for predicting ICP based on clinical and laboratory indicators.

Authors

Jianhu He

Information Center, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Xiaojun Zhu

National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, No. 29 Nanwei Road, Xicheng District, Beijing 100050, China. happyzhuxj@163.com.
Xuan Yang

Dongfang College, Zhejiang University of Finance & Economics, Haining 314408, Zhejiang, China. yx_321@zufe.edu.cn.
Hui Wang

Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.

Keywords

Adult Algorithms Case-Control Studies Cholestasis, Intrahepatic Female Humans Machine Learning Neural Networks, Computer Predictive Value of Tests Pregnancy Pregnancy Complications Retrospective Studies Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (39626970)

Predictive efficacy of machine-learning algorithms on intrahepatic cholestasis of pregnancy based on clinical and laboratory indicators.

Abstract

Authors

Keywords

External Resources

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