Rueangket Ploywarong, Rittiluechai Kristsanamon, Prayote Akara
Department of Obstetrics and Gynecology, Phramongkutklao Hospital, Bangkok, Thailand.
Department of Computer and Information Science, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.
Front Med (Lausanne). 2022 Sep 23;9:976829. doi: 10.3389/fmed.2022.976829. eCollection 2022.
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.
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.
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.
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.
异位妊娠（EP）因其对孕产妇的严重后果而广为人知。早期检测可能决定妊娠结局的生死。我们的目标是在破裂发生前做出快速诊断。因此，研究了使用传统统计方法和机器学习（ML）方法的预测分析模型。
对407例妊娠部位不明（PULs）的病例进行回顾性队列研究：306例用于内部验证，101例用于外部验证，采用嵌套交叉验证技术进行随机分组。基于临床因素、血清标志物和电子病历中的超声检查结果，使用一组22个研究特征，通过神经网络（NNs）、决策树（DT）、支持向量机（SVMs）和统计逻辑回归（LR）进行分析。将诊断性能与曲线下面积（ROC-AUC）进行比较，包括用于决策的敏感性和特异性。
比较预测EP的模型性能（内部验证），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，NNs排名第一，其次是LR、SVMs和DT，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。对于临床辅助，我们报告LR的平均±SD敏感性为：90.20%±3.49%；SVM：89.79%±3.66%；DT：89.22%±4.53%；NNs：86.92%±3.24%。然而，特异性±SD的排名依次为NNs、SVMs、LR和DT，分别为82.02±8.34%、80.37±5.15%、79.65%±6.01%和78.97%±4.07%。
统计方法和ML模型对EP都能实现令人满意的预测。在模型学习中，排名最高的模型是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，NNs可以超越统计方法。这突出了ML在解决具有各种模式的复杂问题以及克服数据泛化误差方面的潜力。
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