Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China - Li, Taishun; Xu, Mingyang; Wang, Yuan; Wang, Ya; Tang, Huirong; Duan, Honglei; Zhao, Guangfeng; Zheng, Mingming; Hu, Yali (Frontiers in Endocrinology, 2024)
Introduction: Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is an effective preventive method against preeclampsia. This study aims to develop a robust and effective preeclampsia prediction model with good performance by machine learning algorithms based on maternal characteristics, biophysical and biochemical markers at 11-13 +6 weeks' gestation, providing an effective tool for early screening