Engineering Research Center of Mobile Health Management Ministry of Education, Hangzhou Normal University, Hangzhou, China.
Hangzhou Hele Tech. Co, Hangzhou, China.
Sci Rep. 2022 Mar 22;12(1):4892. doi: 10.1038/s41598-022-08664-5.
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 (R) 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.
准确的预产期（EDD）有助于孕妇在分娩前做好充分准备，避免分娩时的恐慌。EDD 通常根据末次月经和超声检查的某些公式或由医生估计得出。本研究试图结合产前检查和电子病历，基于梯度提升决策树和门控循环单元（GBDT-GRU）开发一种混合模型。除了探索影响 EDD 的特征外，GBDT-GRU 模型还通过对不同阶段的动态预测获得结果。均方误差（MSE）和决定系数（R）用于比较不同预测方法的性能。此外，我们通过比较不同天数误差下孕妇的比例来评估不同预测模型的预测性能。实验结果表明，混合 GBDT-GRU 模型的性能指标优于其他预测方法，因为它侧重于分析妊娠的时间序列预测因子。本研究的结果有助于制定临床分娩治疗指南，因为它可以帮助临床医生在产科检查中做出正确的决策。
