未经证实:异位妊娠(EP)以其关键的产妇结局而闻名。早期发现可以改变怀孕期间的生与死。我们的目的是在破裂发生之前做出及时的诊断。因此,研究了使用传统统计和机器学习(ML)方法的预测分析模型。
UNASSIGNED:对407例位置未知的妊娠(PUL)进行了回顾性队列研究:306个PUL用于内部验证,101个PUL用于外部验证。用嵌套交叉验证技术随机化。使用一组基于临床因素的22项研究特征,来自电子病历的血清标记和超声检查结果,用神经网络(NN)分析,决策树(DT),支持向量机(SVM),和统计逻辑回归(LR)。将诊断性能与曲线下面积(ROC-AUC)进行比较,包括决策使用的敏感性和特异性。
UNASSIGNED:比较模型性能(内部验证)以预测EP,LR排名第一,平均ROC-AUC±SD为0.879±0.010。在测试数据(外部验证)中,NN排名第一,紧随其后的是LR,SVM,和DT的平均ROC-AUC±SD分别为0.898±0.027、0.896±0.034、0.882±0.029和0.856±0.033。对于临床援助,我们报告LR的平均值±SD的敏感性:90.20%±3.49%;SVM:89.79%±3.66%;DT:89.22%±4.53%;NN:86.92%±3.24%,连续。然而,特异性±SD按NN排序,其次是SVM,LR,和DT,为82.02±8.34%,80.37±5.15%,79.65%±6.01%,和78.97%±4.07%,分别。
UNASSIGNED:统计和ML模型都可以获得令人满意的EP预测。在模型学习中,排名最高的型号是LR,表明EP预测可能具有线性或因果数据模式。然而,在新的测试数据中,神经网络可以克服统计。这凸显了ML在解决各种模式的复杂问题方面的潜力,同时克服了数据的泛化错误。
UNASSIGNED: Ectopic pregnancy (EP) is well known for its critical maternal outcome. Early detection could make the difference between life and death in pregnancy. Our aim was to make a prompt diagnosis before the rupture occur. Thus, the predictive analytical models using both conventional statistics and machine learning (ML) methods were studied.
UNASSIGNED: A retrospective cohort study was conducted on 407 pregnancies with unknown location (PULs): 306 PULs for internal validation and 101 PULs for external validation, randomized with a nested cross-validation technique. Using a set of 22 study features based on clinical factors, serum marker and ultrasound findings from electronic medical records, analyzing with neural networks (NNs), decision tree (DT), support vector machines (SVMs), and a statistical logistic regression (LR). Diagnostic performances were compared with the area under the curve (ROC-AUC), including sensitivity and specificity for decisional use.
UNASSIGNED: Comparing model performance (internal validation) to predict EP, LR ranked first, with a mean ROC-AUC ± SD of 0.879 ± 0.010. In testing data (external validation), NNs ranked first, followed closely by LR, SVMs, and DT with average ROC-AUC ± SD of 0.898 ± 0.027, 0.896 ± 0.034, 0.882 ± 0.029, and 0.856 ± 0.033, respectively. For clinical aid, we report sensitivity of mean ± SD in LR: 90.20% ± 3.49%; SVM: 89.79% ± 3.66%; DT: 89.22% ± 4.53%; and NNs: 86.92% ± 3.24%, consecutively. However, specificity ± SD was ranked by NNs, followed by SVMs, LR, and DT, which were 82.02 ± 8.34%, 80.37 ± 5.15%, 79.65% ± 6.01%, and 78.97% ± 4.07%, respectively.
UNASSIGNED: Both statistics and the ML model could achieve satisfactory predictions for EP. In model learning, the highest ranked model was LR, showing that EP prediction might possess linear or causal data pattern. However, in new testing data, NNs could overcome statistics. This highlights the potency of ML in solving complicated problems with various patterns, while overcoming generalization error of data.