Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters

Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378...

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Detalles Bibliográficos
Autores principales: Esteban-Escaño, Javier, Castán, Berta, Castán, Sergio, Chóliz-Ezquerro, Marta, Asensio, César, Laliena, Antonio R., Sanz-Enguita, Gerardo, Sanz, Gerardo, Esteban, Luis Mariano, Savirón, Ricardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8775221/
https://www.ncbi.nlm.nih.gov/pubmed/35052094
http://dx.doi.org/10.3390/e24010068

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8775221/
https://www.ncbi.nlm.nih.gov/pubmed/35052094
http://dx.doi.org/10.3390/e24010068

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