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Predicting interval from diagnosis to delivery in preeclampsia using electronic health records

Author

Listed:
  • Xiaotong Yang

    (University of Michigan)

  • Hailey K. Ballard

    (University of Florida)

  • Aditya D. Mahadevan

    (University of Florida
    University of Florida)

  • Ke Xu

    (University of Florida)

  • David G. Garmire

    (University of Michigan)

  • Elizabeth S. Langen

    (University of Michigan)

  • Dominick J. Lemas

    (University of Florida
    University of Florida
    University of Florida College of Medicine)

  • Lana X. Garmire

    (University of Michigan
    University of Michigan)

Abstract

Preeclampsia is a major cause of maternal and perinatal mortality with no known cure. Delivery timing is critical to balancing maternal and fetal risks. We develop and externally validate PEDeliveryTime, a class of clinically informative models which resulted from deep-learning models, to predict the time from PE diagnosis to delivery using electronic health records. We build the models on 1533 PE cases from the University of Michigan and validate it on 2172 preeclampsia cases from the University of Florida. PEDeliveryTime full model contains only 12 features yet achieves high c-index of 0.79 and 0.74 on the Michigan and Florida data set respectively. For the early-onset preeclampsia subset, the full model reaches 0.76 and 0.67 on the Michigan and Florida test sets. Collectively, these models perform an early assessment of delivery urgency and might help to better prioritize medical resources.

Suggested Citation

  • Xiaotong Yang & Hailey K. Ballard & Aditya D. Mahadevan & Ke Xu & David G. Garmire & Elizabeth S. Langen & Dominick J. Lemas & Lana X. Garmire, 2025. "Predicting interval from diagnosis to delivery in preeclampsia using electronic health records," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58437-7
    DOI: 10.1038/s41467-025-58437-7
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