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October 1, 2025Open Access

Machine Learning for Dynamic and Short-term Prediction of Preeclampsia Using Routine Clinical and Laboratory Data

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Authors

HLHaoyang LiShanghai UniversityYLYaxin LiCornell UniversityCZChengxi ZangCornell University

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Overview

Retrospective cohort study demonstrates machine learning predicts preeclampsia effectively, indicating improved clinical outcomes.

Key Points

  • Dynamic short-term prediction of preeclampsia is feasible using routine clinical data, enabling timely intervention.
  • Performance peaked at 34 weeks gestational age with AUC reaching 0.863 in training and 0.808-0.834 in validation.
  • Machine learning models employed extreme gradient boosting to analyze over 58,000 pregnancies across multiple sites.
  • The approach emphasizes routine clinical data's utility, suggesting broader applicability in diverse healthcare settings.

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/68dd91cbfe798ba2fc4988a4https://doi.org/10.1101/2025.09.29.25336926
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