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Investigating the interpretability of fetal status assessment using antepartum cardiotocographic records

Authors :
Liting Huang
Zhiying Jiang
Ruichu Cai
Li Li
Qinqun Chen
Jiaming Hong
Zhifeng Hao
Hang Wei
Source :
BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-11 (2021)
Publication Year :
2021
Publisher :
BMC, 2021.

Abstract

Abstract Background 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. Methods 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. Results 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. Conclusions 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.

Details

Language :
English
ISSN :
14726947
Volume :
21
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Medical Informatics and Decision Making
Publication Type :
Academic Journal
Accession number :
edsdoj.7cbc60ce01cb4498be35378160b5c81b
Document Type :
article
Full Text :
https://doi.org/10.1186/s12911-021-01714-4