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Prediction of post-delivery hemoglobin levels with machine learning algorithms.

Sepehr Aghajanian1,2, Kyana Jafarabady1, Mohammad Abbasi1

  • 1Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran.

Scientific Reports
|June 17, 2024
PubMed
Summary

Machine learning models accurately predict postpartum hemoglobin levels using pre-labor clinical data. This aids in predicting postpartum hemorrhage (PPH) risk and enables timely interventions for improved maternal outcomes.

Keywords:
Artificial intelligenceExtreme gradient boostingMachine learningMultilayer perceptronPostpartum hemorrhageSupport vector machine

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Area of Science:

  • Obstetrics and Gynecology
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Predicting postpartum hemorrhage (PPH) before delivery is critical for timely interventions and improved patient outcomes.
  • Existing methods may not fully capture the complexity of predicting PPH risk using readily available clinical data.
  • Machine learning (ML) offers a potential avenue for developing more accurate predictive models.

Purpose of the Study:

  • To utilize machine learning (ML) with pre-labor clinical data and laboratory measurements to predict postpartum hemoglobin (Hb) levels in uncomplicated singleton pregnancies.
  • To identify key predictors of post-delivery Hb levels.
  • To develop and validate an ML model for predicting indirect measures of PPH.

Main Methods:

  • Retrospective analysis of delivery databases from two academic care centers, including 1974 women.
  • Feature selection using Elastic Net regression and Random Forest algorithms to identify significant pre-delivery predictors.
  • Training and evaluation of various ML algorithms, including artificial neural networks (ANN), to predict 24-hour post-delivery Hb levels.

Main Results:

  • Key predictors for post-delivery Hb included parity, gestational age, pre-delivery hemoglobin, fibrinogen levels, and pre-labor platelet count.
  • Artificial Neural Network (ANN) demonstrated the highest accuracy with a Root Mean Squared Error (RMSE) of 0.62.
  • A web-based calculator was developed based on the ANN model: https://predictivecalculators.shinyapps.io/ANN-HB.

Conclusions:

  • Machine learning models can accurately predict post-delivery hemoglobin levels, serving as indirect predictors of postpartum hemorrhage (PPH).
  • The developed ML model and web-based calculator can be integrated into healthcare systems to support clinical decision-making.
  • Further validation with diverse, population-based samples is recommended to enhance model generalizability.