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Related Experiment Video

Updated: Aug 13, 2025

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
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Machine learning approach to predict postpartum haemorrhage: a systematic review protocol.

Banafsheh Boujarzadeh1, Amene Ranjbar2, Farzaneh Banihashemi1

  • 1Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran (the Islamic Republic of).

BMJ Open
|January 19, 2023
PubMed
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This systematic review identifies predictors of postpartum haemorrhage (PPH) using machine learning (ML). The goal is to develop an ML-based algorithm to predict PPH, a major cause of maternal mortality.

Area of Science:

  • Obstetrics and Gynecology
  • Medical Informatics
  • Public Health

Background:

  • Postpartum haemorrhage (PPH) is a critical global health issue, significantly contributing to maternal mortality.
  • Predicting and preventing PPH is crucial for improving maternal outcomes worldwide.

Purpose of the Study:

  • To systematically review and identify predictors of PPH using a machine learning (ML) approach.
  • To propose an ML-based algorithm for predicting PPH.
  • To enhance the early detection and management of PPH.

Main Methods:

  • Systematic literature search across multiple databases (PubMed, EMBASE, Scopus, WOS, IEEE Xplore, Google Scholar) up to December 2022.
  • Inclusion of studies defining PPH and utilizing ML models with performance metrics (AUC, accuracy, precision, sensitivity, specificity).
Keywords:
Maternal medicineOBSTETRICSPREVENTIVE MEDICINEPrenatal diagnosis

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  • Risk of bias and applicability assessment using the PROBAST tool.

    • Main Results:

    • Identification of key predictive factors for PPH through ML analysis.
    • Evaluation of the performance of various ML models in PPH prediction.
    • Summary of evidence on ML applications in PPH prediction.

    Conclusions:

    • Machine learning holds significant potential for predicting postpartum haemorrhage.
    • An ML-based predictive algorithm can aid in the early identification and management of PPH.
    • Further research can refine ML models for improved PPH prediction and maternal care.