A 1D convolutional neural network trained on balanced windowed segments of fetal heart rate traces classifies abnormal birth outcomes with an AUC of 0.86, outperforming classical baselines tested by the authors.
MBRRACE-UK: Mothers and Babies: Reducing Risk through Audits and Confidential Enquiries across the UK
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes
A 1D convolutional neural network trained on balanced windowed segments of fetal heart rate traces classifies abnormal birth outcomes with an AUC of 0.86, outperforming classical baselines tested by the authors.