Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies

PURPOSE: Fetal well-being is usually assessed via fetal heart rate (FHR) monitoring during the antepartum period. However, the interpretation of FHR is a complex and subjective process with low reliability. This study developed a machine learning model that can classify fetal cardiotocography result...

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Detalles Bibliográficos
Autores principales: Park, Tae Jun, Chang, Hye Jin, Choi, Byung Jin, Jung, Jung Ah, Kang, Seongwoo, Yoon, Seokyoung, Kim, Miran, Yoon, Dukyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Yonsei University College of Medicine 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9226828/
https://www.ncbi.nlm.nih.gov/pubmed/35748081
http://dx.doi.org/10.3349/ymj.2022.63.7.692

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9226828/
https://www.ncbi.nlm.nih.gov/pubmed/35748081
http://dx.doi.org/10.3349/ymj.2022.63.7.692

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