Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses-Porto retrospective intrapartum study
IntroductionPerinatal asphyxia is one of the most frequent causes of neonatal mortality, affecting approximately four million newborns worldwide each year and causing the death of one million individuals. One of the main reasons for these high incidences is the lack of consensual methods of early di...
Saved in:
Main Authors: | Maria Ribeiro (Author), Inês Nunes (Author), Luísa Castro (Author), Cristina Costa-Santos (Author), Teresa S. Henriques (Author) |
---|---|
Format: | Book |
Published: |
Frontiers Media S.A.,
2023-03-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Systematic Review of Intrapartum Fetal Heart Rate Spectral Analysis and an Application in the Detection of Fetal Acidemia
by: Luísa Castro, et al.
Published: (2021) -
Clinical Validation of Mobile Cardiotocograph Device for Intrapartum and Antepartum Monitoring Compared to Standard Cardiotocograph: An Inter-Rater Agreement Study
by: Manoja Kumar Das, et al.
Published: (2019) -
Cardiotocographic and Doppler Ultrasonographic Findings in a Fetus with Brain Death Syndrome
by: Yi-Ting Chen, et al.
Published: (2006) -
Predicting asphyxia in term fetus
by: Alev Esercan, et al.
Published: (2023) -
Prenatal Asphyxia in Growth Retarded Fetuses
by: J Gordon Millichap
Published: (1987)