Early prediction of preeclampsia from clinical, multi-omics and laboratory data using random forest model

Early prediction of preeclampsia from clinical, multi-omics and laboratory data using random forest model

Abstract Background Predicting preeclampsia (PE) within the first 16 weeks of gestation is difficult due to various risk factors, poorly understood causes and likely multiple pathogenic phenotypes of preeclampsia.  Objectives In this study, we aimed to develop prediction models for early-onset preec...

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Main Authors: Qiang Zhao, Jia Li, Zhuo Diao, Xiao Zhang, Suihua Feng, Guixue Hou, Wenqiu Xu, Zhiguang Zhao, Zhixu Qiu, Wenzhi Yang, Si Zhou, Peirun Tian, Qun Zhang, Weiping Chen, Huahua Li, Gefei Xiao, Jie Qin, Liqing Hu, Zhongzhe Li, Liang Lin, Shunyao Wang, Ruyun Gao, Wuyan Huang, Xiaohong Ruan, Sufen Zhang, Jianguo Zhang, Lijian Zhao, Rui Zhang
Format: Article
Language:English
Published: BMC 2025-05-01
Series:BMC Pregnancy and Childbirth
Subjects:
Preeclampsia
Proteome
Metabolome
Laboratory data
Random forest
Online Access:https://doi.org/10.1186/s12884-025-07582-4
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https://doi.org/10.1186/s12884-025-07582-4

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