Investigating the interpretability of fetal status assessment using antepartum cardiotocographic records.
Investigating the interpretability of fetal status assessment using antepartum cardiotocographic records.
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DOI：
10.1186/s12911-021-01714-4
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发表时间：
2021-12-20
影响因子：
3.5
通讯作者：
Wei H
中科院分区：
文献类型：
--
作者：
Huang L;Jiang Z;Cai R;Li L;Chen Q;Hong J;Hao Z;Wei H
产程图（CTG）判读在产前胎儿监护中起着至关重要的作用。然而，使用CTG评估胎儿状态的解释主要局限于临床研究。据我们所知，目前还没有对CTG记录的数据分析进行研究，以探索重要的CTG特征与胎儿状态评估之间的因果关系。为了进行分析，2126心电图被自动处理，并通过Sisporto程序测量相应的诊断特征。在本文中，我们的目的是探讨重要的CTG功能和胎儿状态评估之间的因果关系。首先，我们利用数据可视化和斯皮尔曼相关分析来探讨CTG特征之间的关系及其对胎儿状态评估的重要性。其次，我们提出了一个向前逐步选择关联规则分析（ARA），以补充胎儿状态评估规则的基础上稀疏的病理情况。第三，我们建立结构方程模型（SEM），探讨潜在的因果因素和因果系数的胎儿状态评估。数据可视化和斯皮尔曼相关分析发现，13 CTG功能相关的胎儿状态的评价。前向逐步选择ARA进一步验证和补充了胎儿监护指南中的CTG解释规则。基于胎儿监护知识和上述分析，测量模型验证了五个潜在变量，即基线类别（BCat）、变异性类别（VCat）、加速类别（ACat）、减速类别（DCat）和宫缩类别（UCat）。此外，可解释的模型发现了胎儿状态评估的原因因素及其对胎儿状态评估的因果系数。例如，VCat可以预测BCat，UCat也可以预测DCat。ACat、BCat和DCat直接影响胎儿状态评估，其中ACat是重要的因果因素。分析结果揭示了胎儿状态评估的解释规则，找出了影响胎儿状态评估的因素及其因果系数。此外，结果与计算机胎儿监护和临床知识相一致。该方法有助于循证医学研究，实现智能胎儿监护。
Cardiotocography (CTG) interpretation plays a critical role in prenatal fetal monitoring. However, the interpretation of fetal status assessment using CTG is mainly confined to clinical research. To the best of our knowledge, there is no study on data analysis of CTG records to explore the causal relationships between the important CTG features and fetal status evaluation. For analyses, 2126 cardiotocograms were automatically processed and the respective diagnostic features measured by the Sisporto program. In this paper, we aim to explore the causal relationships between the important CTG features and fetal status evaluation. First, we utilized data visualization and Spearman correlation analysis to explore the relationship among CTG features and their importance on fetal status assessment. Second, we proposed a forward-stepwise-selection association rule analysis (ARA) to supplement the fetal status assessment rules based on sparse pathological cases. Third, we established structural equation models (SEMs) to investigate the latent causal factors and their causal coefficients to fetal status assessment. Data visualization and the Spearman correlation analysis found that thirteen CTG features were relevant to the fetal state evaluation. The forward-stepwise-selection ARA further validated and complemented the CTG interpretation rules in the fetal monitoring guidelines. The measurement models validated the five latent variables, which were baseline category (BCat), variability category (VCat), acceleration category (ACat), deceleration category (DCat) and uterine contraction category (UCat) based on fetal monitoring knowledge and the above analyses. Furthermore, the interpretable models discovered the cause factors of fetal status assessment and their causal coefficients to fetal status assessment. For instance, VCat could predict BCat, and UCat could predict DCat as well. ACat, BCat and DCat directly affected fetal status assessment, where ACat was the important causal factor. The analyses revealed the interpretation rules and discovered the causal factors and their causal coefficients for fetal status assessment. Moreover, the results are consistent with the computerized fetal monitoring and clinical knowledge. Our approaches are conducive to evidence-based medical research and realizing intelligent fetal monitoring.
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影响因子：
0.8
作者：
Alsolami, Fawaz;Amin, Talha;Zabinski, Krzysztof
通讯作者：
Zabinski, Krzysztof
影响因子：
2.4
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
Carcopino, Xavier
影响因子：
4.3
作者：
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通讯作者：
Da Graca, Luis M.
影响因子：
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作者：
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通讯作者：
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