摘要
目的探讨人工神经网络在预测胎儿宫内窘迫中的应用价值。方法选取2013年6月至2015年6月南京市浦口医院因分娩前诊断为胎儿窘迫而行产科干预分娩,新生儿Apgar评分<8分的产妇198例,将全部样本按奇偶数分为两组(训练组99例,验证组99例)。选择11个输入参数,羊水性状:清、Ⅰ度、Ⅱ度、Ⅲ度,胎心监护:正常、基线变异减弱或消失、基线>160次/min、基线<110次/min、早期减速、变异减速、晚期减速。将训练组用数值法构建网络,然后以验证组来测试诊断符合率。另取同期因分娩前诊断胎儿窘迫而行产科干预,分娩后未发现胎儿宫内窘迫依据的产妇220例为临床假阳性组,输入参数测试假阳性率。结果验证组诊断符合率87.88%,假阴性率12.12%;假阳性组假阳性率20.53%,诊断符合率79.47%。结论采用人工神经网络预测胎儿窘迫有很好的研究价值和应用前景,增加临床检测指标及样本量的输入可能会进一步提高网络精度。
Objective To explore the application value of artificial neural network in the prediction of fetal distress. Methods A total of 198 maternal cases who were diagnosed as fetal distress before delivery,then received obstetric intervention in our hospital,and the neonatal Apgar score of whom was below 8 points were recruited,and all samples were randomly divided into two groups,with 99 cases in the training group and 99 cases in the validation group. Eleven input parameters were selected,namely,the character of amniotic fluid: clear,first degree,second degree and third degree. Fetal heart monitoring: normal,baseline variability weakened or disappeared,baseline > 160 beats / min,baseline < 110 beats per minute,early deceleration,deceleration variation,and advanced deceleration. The training group was constructed by numerical method,and then the validation group was used to test the diagnostic accuracy. Meanwhile,another 220 cases with prenatal diagnosis of fetal distress were as clinical false positive group,who were admitted to the hospital at the same period and received the obstetric intervention before delivery while after delivery did not show the intrauterine fetal distress,then the parameters were input to test the false positive rate. Results The diagnostic accordance rate was 87. 88%,the false negative rate was 12. 12%,the false positive rate was only 20. 53%,and the diagnosis coincidence rate was 79. 47%. Conclusion The application of artificial neural network to the prediction of fetal distress has research value and application prospects; increasing the input of clinical detection parameters and sample size may further improve the accuracy of the network.
出处
《安徽医学》
2016年第10期1201-1203,共3页
Anhui Medical Journal
基金
南京市医学科技发展基金项目(项目编号:ykk13209)
关键词
人工神经网络
胎儿窘迫
预测
Artificial neural network
Fetal distress
Prediction