Department of Obstetrics and Gynaecology, Jordan University of Science and Technology, Jordan.
King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan.
Comput Biol Chem. 2020 Apr;85:107233. doi: 10.1016/j.compbiolchem.2020.107233. Epub 2020 Feb 15.
Preterm birth, defined as a delivery before 37 weeks' gestation, continues to affect 8-15% of all pregnancies and is associated with significant neonatal morbidity and mortality. Effective prediction of timing of delivery among women identified to be at significant risk for preterm birth would allow proper implementation of prophylactic therapeutic interventions. This paper aims first to develop a model that acts as a decision support system for pregnant women at high risk of delivering prematurely before having cervical cerclage. The model will predict whether the pregnancy will continue beyond 26 weeks' gestation and the potential value of adding the cerclage in prolonging the pregnancy. The second aim is to develop a model that predicts the timing of spontaneous delivery in this high risk cohort after cerclage. The model will help treating physicians to define the chronology of management in relation to the risk of preterm birth, reducing the neonatal complications associated with it. Data from 274 pregnancies managed with cervical cerclage were included. 29 of the procedures involved multiple pregnancies. To build the first model, a data balancing technique called SMOTE was applied to overcome the problem of highly imbalanced class distribution in the dataset. After that, four classification models, namely Decision Tree, Random Forest, K-Nearest Neighbors (K-NN), and Neural Network (NN) were used to build the prediction model. The results showed that Random Forest classifier gave the best results in terms of G-mean and sensitivity with values of 0.96 and 1.00, respectively. These results were achieved at an oversampling ratio of 200%. For the second prediction model, five classification models were used to predict the time of spontaneous delivery; linear regression, Gaussian process, Random Forest, K-star, and LWL classifier. The Random Forest classifier performed best, with 0.752 correlation value. In conclusion, computational models can be developed to predict the need for cerclage and the gestation of delivery after this procedure. These models have moderate/high sensitivity for clinical application.
早产是指在妊娠 37 周前分娩，继续影响 8-15%的所有妊娠，并与新生儿发病率和死亡率显著相关。在识别出有早产高风险的女性中，有效预测分娩时间将允许适当实施预防性治疗干预措施。本文的主要目的是首先开发一种模型，作为在进行宫颈环扎术之前，对有早产高风险的孕妇进行决策支持系统。该模型将预测妊娠是否会继续超过 26 周妊娠，并预测环扎术是否可以延长妊娠。第二个目的是开发一种模型，预测高危队列在宫颈环扎术后的自然分娩时间。该模型将帮助治疗医生根据早产风险确定管理的时间顺序，从而减少与之相关的新生儿并发症。纳入了 274 例接受宫颈环扎术管理的妊娠数据。其中 29 例涉及多胎妊娠。为了建立第一个模型，应用了一种称为 SMOTE 的数据平衡技术，以克服数据集高度不平衡的类分布问题。之后，使用了四种分类模型，即决策树、随机森林、K-最近邻（K-NN）和神经网络（NN）来建立预测模型。结果表明，随机森林分类器在 G-均值和灵敏度方面表现最佳，值分别为 0.96 和 1.00。这些结果是在过采样率为 200%的情况下实现的。对于第二个预测模型，使用了五种分类模型来预测自然分娩的时间：线性回归、高斯过程、随机森林、K-星和 LWL 分类器。随机森林分类器表现最佳，相关值为 0.752。总之，可以开发计算模型来预测宫颈环扎术的需求和该手术之后的分娩时间。这些模型具有中等/高度的临床应用灵敏度。
