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 (Preterm PE) were developed using maternal characteristics and laboratory data.

Methods

Between January 2019 and December 2021, we retrospectively recruited 144 EOPE, 363 LOPE, 231 Preterm PE, and 1458 healthy participants from six hospitals. We utilized all available clinical and laboratory data obtained during routine prenatal visits in early pregnancy. The models for EOPE, LOPE, and Preterm PE were created using ensemble machine learning models with patient clinical and laboratory data.

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SUBMITTER: Chen S 

PROVIDER: S-EPMC12044989 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature