Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model

Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model

Abstract Background This study was performed to characterize the relationship of various laboratory test indicators with clinical information and Preeclampsia (PE) development. Then, prediction models for early-onset preeclampsia (EOPE), late-onset preeclampsia (LOPE), and preterm preeclampsia (Pret...

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Main Authors: Songchang Chen, Jia Li, Xiao Zhang, Wenqiu Xu, Zhixu Qiu, Siyao Yan, Wenrui Zhao, Zhiguang Zhao, Peirun Tian, Qiang Zhao, Qun Zhang, Weiping Chen, Huahua Li, Xiaohong Ruan, Gefei Xiao, Sufen Zhang, Liqing Hu, Jie Qin, Wuyan Huang, Zhongzhe Li, Shunyao Wang, Rui Zhang, Shang Huang, Xin Wang, Yao Yao, Jian Ran, Danling Cheng, Qi Luo, Teng Pan, Ruyun Gao, Jing Zheng, Yuxuan Wang, Cong Liu, Xianling Cao, Xuanyou Zhou, Naixin Xu, Lanlan Zhang, Xu Han, Haolin Wang, Suihua Feng, Shuyuan Li, Jianguo Zhang, Lijian Zhao, Fengxiang Wei
Format: Article
Language:English
Published: BMC 2025-05-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Preeclampsia
Laboratory data
Machine learning model
Online Access:https://doi.org/10.1186/s12911-025-02999-5
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https://doi.org/10.1186/s12911-025-02999-5

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