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Understanding birthing mode decision making using artificial neural networks.

机译:了解使用人工神经网络进行的分娩模式决策。

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BACKGROUND: This study examined obstetricians' decisions to perform or not to perform cesarean sections. The aim was to determine whether an artificial neural network could be constructed to accurately and reliably predict the birthing mode decisions of expert clinicians and to elucidate which factors were most important in deciding the birth mode. METHODS: Mothers with singleton, live births who were privately insured, nonclinic, non-Medicaid patients at a major tertiary care private hospital were included in the study (N = 1508). These mothers were patients of 2 physician groups: a 7-obstetrician multispecialtygroup practice and a physician group of 79 independently practicing obstetricians affiliated with the same hospital. A feedforward, multilayer artificial neural network (ANN) was developed and trained. It was then tested and optimized until the most parsimonious network was identified that retained a similar level of predictive power and classification accuracy. The performance of this network was further optimized using the methods of receiver operating characteristic (ROC) analysis and information theory to find the cutoff that maximized the information gain. The performance of the final ANN at this cutoff was measured using sensitivity, specificity, classification accuracy, area under the ROC curve, and maximum information gain. RESULTS: The final neural network had excellent predictive accuracy for the birthing mode (classification accuracy = 83.5%; area under the ROC curve = 0.924; maximum information = 40.4% of a perfect diagnostic test). CONCLUSION: This study demonstrated that a properly optimized ANN is able to accurately predict the birthing mode decisions of expert clinicians. In addition to previously identified clinical factors (cephalopelvic disproportion, maternal medical condition necessitating a cesarean section, arrest of labor, malpresentation of the baby, fetal distress, andfailed induction), nonclinical factors such as the mothers' views on birthing mode were also found to be important in determining the birthing mode.
机译:背景:这项研究检查了产科医生决定进行或不进行剖宫产的决定。目的是确定是否可以构建人工神经网络来准确可靠地预测专家临床医生的分娩方式决定,并阐明哪些因素对决定分娩方式最重要。方法:本研究纳入了在一家大型三级私立医院接受私人保险,非诊所,非医疗补助患者的单胎活产母亲(N = 1508)。这些母亲是2个医师小组的患者:7名妇产科医生的多专业小组执业医师和79名隶属于同一家医院的独立执业医师的医师分组。前馈,多层人工神经网络(ANN)的开发和培训。然后对其进行测试和优化,直到确定出最简约的网络,其保留了相似水平的预测能力和分类准确性。使用接收器工作特性(ROC)分析和信息论的方法进一步优化了该网络的性能,以找到使信息增益最大化的截止点。使用敏感性,特异性,分类准确性,ROC曲线下的面积和最大信息增益来测量最终ANN的性能。结果:最终的神经网络对分娩模式具有出色的预测准确性(分类准确性= 83.5%; ROC曲线下的面积= 0.924;最大信息=完美诊断测试的40.4%)。结论:这项研究表明,经过适当优化的人工神经网络能够准确预测专家临床医生的分娩方式决策。除了先前确定的临床因素(头盆骨比例失调,需要剖宫产的孕产妇疾病,停产,婴儿畸形,胎儿窘迫和引产失败)之外,还发现了非临床因素,例如母亲对分娩方式的看法。在确定分娩方式方面很重要。

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