Estimated date of delivery based on second trimester fetal head circumference: A population‐based validation of 21 451 deliveries

Zou³

et al. 2021

Preprint

Background: An accurate estimated date of delivery (EDD) helps pregnant women make adequate preparations before delivery and avoid the panic of parturition. EDD is normally derived from some formulates or estimated by doctors based on last menstruation period and ultrasound examinations. The main aim of this study was to develop a hybrid model to improve the accuracy of EDD and promote the health and safety of pregnant women and fetuses. Methods: This study attempted to combine antenatal examinations and electronic medical records to develop a hybrid model based on Gradient Boosting Decision Tree and Gated Recurrent Unit (GBDT-GRU). Besides exploring the features that affect the EDD, GBDT-GRU model obtained the results by dynamic prediction of different stages. The mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used to compare the performance among the different prediction methods. In addition, we evaluated predictive performances of different prediction models by comparing the proportion of pregnant women under the error of different days. Results: The clinical data were collected with 33,222 pregnancy examination records from 5537 Chinese pregnant women who have given birth. Experimental results showed that the hybrid GBDT-GRU model outperformed other prediction methods with coefficient of determination (R2) of 0.84, mean square error (MSE) of 41.73. We also found that the GBDT-GRU model had a smaller deviation by comparing the bias between the actual delivery date and the EDD under different methods. Conclusions: In comparison with other prediction methods, the GBDT-GRU model provided better performance results. The results of this study are helpful for the development of guidelines for clinical delivery treatments, as it can assist clinicians in making correct decisions during obstetric examinations.

“…The clinical research about EDD was still focused on ultrasound and LMP, such as head circumference [29], cervical length, some improved formula methods [30] and so on [31,32].…”

Section: Discussionmentioning

confidence: 99%

Estimated Date of Delivery with Electronic Medical Records by a Hybrid GBDT-GRU Model

Zou³

et al. 2021

Preprint

“…The clinical research about EDD was still focused on ultrasound and LMP, such as head circumference 29 , cervical length, some improved formula methods 30 and so on 31,32 . These studies provided a reference for feature selection of machine learning.…”

Section: Discussionmentioning

confidence: 99%

Estimated Date of Delivery With Electronic Medical Records By A Hybrid GBDT-GRU Model

Xu³

et al. 2021

Preprint

An accurate estimated date of delivery (EDD) helps pregnant women make adequate preparations before delivery and avoid the panic of parturition. EDD is normally derived from some formulates or estimated by doctors based on last menstruation period and ultrasound examinations. The main aim of this study was to develop a hybrid model to improve the accuracy of EDD and promote the health and safety of pregnant women and fetuses. This study attempted to combine antenatal examinations and electronic medical records to develop a hybrid model based on Gradient Boosting Decision Tree and Gated Recurrent Unit (GBDT-GRU). Besides exploring the features that affect the EDD, GBDT-GRU model obtained the results by dynamic prediction of different stages. The mean square error (MSE) and coefficient of determination (R2) were used to compare the performance among the different prediction methods. In addition, we evaluated predictive performances of different prediction models by comparing the proportion of pregnant women under the error of different days. The clinical data were collected with 33,222 pregnancy examination records from 5537 Chinese pregnant women who have given birth. Experimental results showed that the hybrid GBDT-GRU model outperformed other prediction methods with coefficient of determination (R2) of 0.84, mean square error (MSE) of 41.73. We also found that the GBDT-GRU model had a smaller deviation by comparing the bias between the actual delivery date and the EDD under different methods. In comparison with other prediction methods, the GBDT-GRU model provided better performance results. The results of this study are helpful for the development of guidelines for clinical delivery treatments, as it can assist clinicians in making correct decisions during obstetric examinations.

“…In several studies, calculating EDD by ultrasound of the first trimester of pregnancy is more accurate than the LMP 7 , 8 . The research of Kessler et al 9 aimed at assessing the actual pregnancy length and accuracy of EDD prediction based on fetal head circumference measured at the second trimester, a population-based validation of 21,451 deliveries showed measurements can be safely used to predict EDD. Majola et al 10 compared the accuracy of LMP recall and an early ultrasound (EUS) in predicting the EDD in South African pregnant women, the results show that the effect of using EUS to calculate EDD is obviously better.…”

Section: Introductionmentioning

confidence: 99%

Estimated date of delivery with electronic medical records by a hybrid GBDT-GRU model

Xu³

et al. 2022

Sci Rep

An accurate estimated date of delivery (EDD) helps pregnant women make adequate preparations before delivery and avoid the panic of parturition. EDD is normally derived from some formulates or estimated by doctors based on last menstruation period and ultrasound examinations. This study attempted to combine antenatal examinations and electronic medical records to develop a hybrid model based on Gradient Boosting Decision Tree and Gated Recurrent Unit (GBDT-GRU). Besides exploring the features that affect the EDD, GBDT-GRU model obtained the results by dynamic prediction of different stages. The mean square error (MSE) and coefficient of determination (R2) were used to compare the performance among the different prediction methods. In addition, we evaluated predictive performances of different prediction models by comparing the proportion of pregnant women under the error of different days. Experimental results showed that the performance indexes of hybrid GBDT-GRU model outperformed other prediction methods because it focuses on analyzing the time-series predictors of pregnancy. The results of this study are helpful for the development of guidelines for clinical delivery treatments, as it can assist clinicians in making correct decisions during obstetric examinations.