Background Globally, the preterm birth rate has tended to increase over time. Ultrasonography cervical-length assessment is considered to be the most effective screening method for preterm birth, but routine, universal cervical-length screening remains controversial because of its cost. Objective We used obstetric data to analyze and assess the risk of preterm birth. A machine learning model based on time-series technology was used to analyze regular, repeated obstetric examination records during pregnancy to improve the performance of the preterm birth screening model. Methods This study attempts to use continuous electronic medical record (EMR) data from pregnant women to construct a preterm birth prediction classifier based on long short-term memory (LSTM) networks. Clinical data were collected from 5187 pregnant Chinese women who gave birth with natural vaginal delivery. The data included more than 25,000 obstetric EMRs from the early trimester to 28 weeks of gestation. The area under the curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the prediction model. Results Compared with a traditional cross-sectional study, the LSTM model in this time-series study had better overall prediction ability and a lower misdiagnosis rate at the same detection rate. Accuracy was 0.739, sensitivity was 0.407, specificity was 0.982, and the AUC was 0.651. Important-feature identification indicated that blood pressure, blood glucose, lipids, uric acid, and other metabolic factors were important factors related to preterm birth. Conclusions The results of this study will be helpful to the formulation of guidelines for the prevention and treatment of preterm birth, and will help clinicians make correct decisions during obstetric examinations. The time-series model has advantages for preterm birth prediction.

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.

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.

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.

BACKGROUND Globally, the trends of preterm birth rate have been increasing over time. Ultrasonography cervical length assessment is considered to be the most effective screening method, however, universal cervical length screening in the whole population remains controversial because of the cost budget. OBJECTIVE In this work, obstetric data are used to analyze and assess the risk of preterm birth. The purpose is to screen high-risk groups of preterm birth in the early and second trimester of pregnancy, and targeted cervical screening is more in line with health economics. METHODS This study attempts to use continuous electronic medical records(EMRs) data of pregnant women to construct a preterm birth predicting classifier based on long short-term memory (LSTM) networks. The clinical data were collected from 5187 Chinese pregnant women with natural vaginal delivery, including more than 25,000 obstetric EMRs during the early trimester to 28 weeks of gestation. The area under ROC curve, accuracy, sensitivity and specificity were used to assess the performance of prediction model. RESULTS Compared with traditional cross-sectional study, LSTM model in time series study has better overall prediction ability, which has a lower misdiagnosis rate with the same detection rate, and the accuracy was 0.739, sensitivity was 0.407, specificity was 0.982, and AUC was 0.651. Feature importance identification indicated that blood pressure, blood glucose, lipids, uric acid and other metabolic factors were the important factors related to preterm birth. CONCLUSIONS The results of this study are helpful to the formulation of guidelines for the prevention and treatment of preterm birth, and it can assist the clinicians to make correct decisions during the obstetric examinations. For the preterm birth prediction scenario, time series model has certain advantages.