Construction of intelligent regulation model of oxytocin in vaginal birth after cesarean
HU Tingting1, 2,ZHANG Yichao3, YUAN Zhenming3, LI Jianhong4, LU Zhongqiu5, ZHU Xiaoling2
1.Deyang People’s Hospital, Deyang 618000, China; 2.College of Nursing, Wenzhou Medical University, Wenzhou 325035, China;3.Department of Medicine, Hangzhou Normal University, Hangzhou 310000, China; 4.Information Center, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; 5.Department of Emergency,the First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325015, China
HU Tingting,ZHANG Yichao,YUAN Zhenming, et al. Construction of intelligent regulation model of oxytocin in vaginal birth after cesarean[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2022, 52(4): 266-271.
Abstract：Objective: To construct an intelligent regulation model of oxytocin (OT) in vaginal trial delivery of (VBAC) after cesarean section and to further explore the intelligent and precise regulation and control scheme for the use of OT during delivery. Methods: A cross-sectional study design was used to collect the data of VBAC parturients who delivered in the obstetrics department of the First Affiliated Hospital of Wenzhou Medical University from January 2014 to May 2020 and used OT during the labor process. The multiple linear regression method was used to screen the modeling variables from the electronic medical records, and combined with the variables such as fetal heart rate and uterine contraction frequency extracted by fetal heart rate, the OT drip speed prediction model was established based on XGBoost algorithm. At the same time, compared with Logistic regression model and traditional decision tree, the data set was divided as training set and test set in a ratio of 8:2, and the prediction performance of the model was evaluated in terms of accuracy, precision, recall rate and F1 score. Results: A total of 1 005 OT regulation records were included in 124 parturients with VBAC. The performance of XGBoost model was the best, with the under -50% discount cross-validation, accuracy, precision,recall and F1 value of the model being 0.82, 0.84, 0.80 and 0.82. The order of variable importance showed that the duration of uterine contraction, uterine pressure, fetal heart rate, frequency of uterine contraction and interval time of previous cesarean section were variables with great contributions to modeling. Conclusion: In this study,an OT regulation model is constructed based on XGBoost. It has the advantages of fast training speed, high accuracy and strong extrapolation, so it is of positive significance to assist the midwifery in the decision-making of OT injection.
