Machine learning algorithms to predict early pregnancy loss after in vitro fertilization-embryo transfer with fetal heart rate as a strong predictor

2020 ◽  
Vol 196 ◽  
pp. 105624 ◽  
Author(s):  
Lijue Liu ◽  
Yongxia Jiao ◽  
Xihong Li ◽  
Yan Ouyang ◽  
Danni Shi
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Fetal Heart Rate ◽  
Pregnancy Loss ◽  
Fetal Heart ◽  
Strong Predictor ◽  
Machine Learning Algorithms ◽  
Early Pregnancy Loss ◽  
Vitro Fertilization

scholarly journals Fetal Heart Rate Analysis for Automatic Detection of Perinatal Hypoxia Using Normalized Compression Distance and Machine Learning

2017 ◽  
Vol 8 ◽  
Author(s):  
Óscar Barquero-Pérez ◽  
Ricardo Santiago-Mozos ◽  
José M. Lillo-Castellano ◽  
Beatriz García-Viruete ◽  
Rebeca Goya-Esteban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Automatic Detection ◽  
Perinatal Hypoxia ◽  
Rate Analysis ◽  
Normalized Compression Distance ◽  
Heart Rate Analysis

scholarly journals A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

2018 ◽  
Vol 7 (8) ◽  
pp. 223 ◽  
Author(s):  
Zhidong Zhao ◽  
Yang Zhang ◽  
Yanjun Deng
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Medical Decision ◽  
Support Vector ◽  
Visual Interpretation ◽  
Adaptive Boosting ◽  
Original Dataset

Continuous monitoring of the fetal heart rate (FHR) signal has been widely used to allow obstetricians to obtain detailed physiological information about newborns. However, visual interpretation of FHR traces causes inter-observer and intra-observer variability. Therefore, this study proposed a novel computerized analysis software of the FHR signal (CAS-FHR), aimed at providing medical decision support. First, to the best of our knowledge, the software extracted the most comprehensive features (47) from different domains, including morphological, time, and frequency and nonlinear domains. Then, for the intelligent assessment of fetal state, three representative machine learning algorithms (decision tree (DT), support vector machine (SVM), and adaptive boosting (AdaBoost)) were chosen to execute the classification stage. To improve the performance, feature selection/dimensionality reduction methods (statistical test (ST), area under the curve (AUC), and principal component analysis (PCA)) were designed to determine informative features. Finally, the experimental results showed that AdaBoost had stronger classification ability, and the performance of the selected feature set using ST was better than that of the original dataset with accuracies of 92% and 89%, sensitivities of 92% and 89%, specificities of 90% and 88%, and F-measures of 95% and 92%, respectively. In summary, the results proved the effectiveness of our proposed approach involving the comprehensive analysis of the FHR signal for the intelligent prediction of fetal asphyxia accurately in clinical practice.

scholarly journals Low endometrial volume may predict early pregnancy loss in women undergoing in vitro fertilization

2007 ◽  
Vol 24 (6) ◽  
pp. 259-261 ◽  
Author(s):  
Efraim Zohav ◽  
Raoul Orvieto ◽  
Eyal Y. Anteby ◽  
Octav Segal ◽  
Simion Meltcer ◽  
...  
Keyword(s):  
In Vitro Fertilization ◽  
Early Pregnancy ◽  
Pregnancy Loss ◽  
Early Pregnancy Loss ◽  
Vitro Fertilization ◽  
Endometrial Volume

Early pregnancy loss and obstetrical risk after in-vitro fertilization and embryo replacement

1988 ◽  
Vol 3 (5) ◽  
pp. 671-675 ◽  
Author(s):  
P. Barlow ◽  
B. Lejeune ◽  
F. Puissant ◽  
Y. Englert ◽  
M. Van Rysselberge ◽  
...  
Keyword(s):  
In Vitro Fertilization ◽  
Early Pregnancy ◽  
Pregnancy Loss ◽  
Early Pregnancy Loss ◽  
Vitro Fertilization ◽  
Embryo Replacement
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