基于深度神经网络原理构建宫腔镜下瘢痕部位妊娠物清除术时大出血风险预测模型  

Based on the deep neural network principle,construct prediction model for massive bleeding risk during hysteroscopic removal of scar gestation

作　　者：莫坚[1] 黄建邕[1] 刘昊[1] 韦羽梅 罗伟 马娅芬[2] Mo Jian;Huang Jianyong;Liu Hao;Wei Yumei;Luo Wei;Ma Yafen(Department of and Gynecology,Nanning First Peoples Hospital,Nanning Guangxi 530022,P.R.China;Department of Obstetrics and Gynecology,Nanning Second People's Hospital,Nanning Guangxi 530022,P.R.China)

机构地区：[1]南宁市第一人民医院妇科,广西南宁530022 [2]南宁市第二人民医院妇科,广西南宁530022

出　　处：《中国计划生育和妇产科》2023年第3期72-77,共6页Chinese Journal of Family Planning & Gynecotokology

基　　金：南宁市科学研究与技术开发计划项目(项目编号:20173017-3)。

摘　　要：目的 探索基于深度神经网络原理构建宫腔镜下剖宫产瘢痕部位妊娠物清除术时大出血发生风险的预测模型,为预测大出血发生风险提供参考。方法 选取南宁市第一人民医院、南宁市第二人民医院2016年1月至2019年7月收治的200例剖宫产瘢痕妊娠(cesarean scar pregnancy, CSP)患者为数据集,收集所有入选患者的临床资料,随机选取数据集的60%为训练集,40%为测试集,对训练集和测试集中的出血组与对照组进行参数比较;根据模糊数学理论对数据集中的临床指标进行量化处理,采用R语言neuralnet包构建深度神经网络训练平台,预测行宫腔镜下瘢痕部位妊娠物清除术时大出血发生风险,并验证模型正确率和准确度。结果 训练集中出血组和对照组患者在住院次数、年龄、停经时间、术前凝血酶原时间(prothrombin time, PT)、活化部分凝血活酶时间(activated partial thromboplastin time, APTT)、凝血酶时间(thrombin time, TT)、纤维蛋白原(fibrinogen, Fib)、术前β-人绒毛膜促性腺激素(beta human chorionic gonadotropin, β-hCG)水平、B超孕囊最大径线、B超子宫瘢痕处肌层厚度、B超临床分型、手术时间的比较上,差异均有统计学意义(P<0.05);测试集中出血组和对照组患者在住院次数、术前PT、术前APTT、术前TT、术前Fib、B超孕囊最大径线、B超子宫瘢痕肌层厚度、B超临床分型及术前β-hCG水平的比较上,差异均有统计学意义(P<0.05)。利用深度神经网络构建的宫腔镜下瘢痕妊娠物清除术时大出血发生风险的预测模型中训练样集本120例,测试集样本80例,深度神经网络模型对数据分类的准确率均达到100.00%。在保证模型的稳定性和泛化性的前提下,通过模型混淆矩阵分析得出:训练集的准确度为0.925,敏感度为0.918,特异度为0.932,召回率为0.918,精确率为0.933。为保证模型预测的一致性和准确性,采用测试集进行验证,最终确定�ObjectiveTo explore the construction of a prediction model based on the principle of deep neural network to treat the risk of massive bleeding during cesarean scar pregnancy by hysteroscopic removal of pregnancy material,in order to predict cesarean scar pregnancy,to provide reference for the risk of massive hemorrhage.Methods 200 cases of cesarean scar pregnancy(CSP)patients predicting admitted to Nanning First People's Hospital and Nanning Second Peoples Hospital from January 2016 to July 2019 were selected as the data set,clinical data of all patients were collected,60%of the data set was randomly selected as the training set and 40%as the test set,the parameters of the bleeding group and the control group in the training set and sest set were compared.The clinical indicators in the data set were quantized according to the fuzzy mathematical theory,and the R language neuralnet package was used to build a deep neural network training plaform to predict the risk of massive bleeding during hysteroscopic removal of scar gestation in the treatment of CSP,and to verify the correctness and accuracy of the model.Results There were statistically significant differences in thrombin time,age,duration of menopause,preoperative prothrombin time(PT),and activated partial thromboplastin time(APTT),thrombin time(TT),fibrinogen(Fib),and preoperative beta human chorionic gonadotropin(β-hCG)level,maximum diameter of gestational sac by B-ultrasound,muscular layer thickness at uterine scar,clinical classification of B-ultrasound and operation time between the bleeding group and control group in the training set(P<0.05);There were statistically significant differences in hospitalization times,preoperative PT,preoperative APTT,preoperative TT,preoperative Fib,maximum diameter of gestational sac by Bultrasound,thickness of uterine cicatoid muscle layer by B-ultrasound,clinical classification of B-ultrasound and preoperativeβ-hCG level between the bleeding group and control group(P<0.05).In the prediction model of the risk of massiv

关 键 词：深度神经网络 宫腔镜妊娠物清除术 剖宫产瘢痕部位妊娠 大出血 风险评估 

分 类 号：R713.8[医药卫生—妇产科学]