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Related Experiment Video

Updated: Oct 4, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review.

Ayleen Bertini1,2, Rodrigo Salas3,4,5, Steren Chabert3,4,5

  • 1Metabolic Diseases Research Laboratory (MDRL), Interdisciplinary Center for Research in Territorial Health of the Aconcagua Valley (CIISTe Aconcagua), Center for Biomedical Research (CIB), Universidad de Valparaíso, Valparaiso, Chile.

Frontiers in Bioengineering and Biotechnology
|February 7, 2022
PubMed
Summary

Machine learning accurately predicts perinatal complications using electronic health records and medical images. This technology shows promise for improving maternal and infant health outcomes.

Keywords:
artificial intelligencemachine learningperinatal complicationsprediction modelpredictive toolpregnancy

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Area of Science:

  • Obstetrics and Gynecology
  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Artificial intelligence (AI) and machine learning (ML) are increasingly utilized in healthcare for prediction and diagnosis.
  • ML methods are integral to tools in obstetrics and childcare, highlighting their relevance in perinatal care.

Purpose of the Study:

  • To systematically review and summarize machine learning techniques for predicting perinatal complications.
  • To evaluate the applicability and performance of ML methods in identifying pregnancy complications.

Main Methods:

  • A systematic literature search was conducted across PubMed, Scopus, and Web of Science using keywords related to machine learning and perinatal complications.
  • PRISMA guidelines were followed to manage and classify 98 initially identified articles, with 31 ultimately selected based on inclusion/exclusion criteria.

Main Results:

  • Electronic medical records (48%), medical images (29%), and biological markers (19%) were primary data sources for ML predictions.
  • The most studied complications were pre-eclampsia and prematurity, with ML models achieving high accuracy, such as 95.7% for prematurity prediction using support vector machines and 99.7% for neonatal mortality using XGBoost.

Conclusions:

  • Continued research and development of multicenter clinical applications for ML are crucial to reduce perinatal complications.
  • This review contributes valuable insights into the application of AI and ML in women's health, particularly in the perinatal period.