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Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters.
Javier Esteban-Escaño1, Berta Castán2, Sergio Castán3
1Department of Electronic Engineering and Communications, Escuela Universitaria Politécnica de La Almunia, Universidad de Zaragoza, Calle Mayor 5, 50100 La Almunia de Doña Godina, Spain.
Entropy (Basel, Switzerland)
|January 21, 2022
Summary
Machine learning accurately predicts neonatal acidemia from electronic fetal monitoring (EFM) signals. This tool can help prevent unnecessary cesarean sections by identifying high-risk cases.
Area of Science:
- Perinatal medicine
- Machine learning in healthcare
- Fetal physiology
Background:
- Electronic fetal monitoring (EFM) is standard for intrapartum fetal surveillance.
- Predicting neonatal acidemia remains a critical challenge in obstetrics.
Purpose of the Study:
- To develop and validate machine learning models for predicting neonatal acidemia using EFM data.
- To assess the clinical utility of these models in preventing unnecessary interventions.
Main Methods:
- A case-control study involving 378 infants.
- Logistic regression, random forest, and neural network models were trained on EFM signal features.
- Model performance was evaluated using discrimination, calibration, and clinical utility metrics.
Main Results:
- A random forest model achieved an AUC of 0.865.
- The model demonstrated good discrimination and calibration.
- At a 33% cutoff, the model could prevent 46% of unnecessary cesarean sections while missing 5% of acidotic cases.
Conclusions:
- EFM signal features can be used to build accurate predictive models for neonatal acidemia.
- This approach offers a practical tool to reduce unnecessary cesarean sections.
- Machine learning models show promise for improving intrapartum fetal surveillance.
