دورية أكاديمية
Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making
| العنوان: | Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making |
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| المؤلفون: | Cherng Chia Yang, Ching Fu Wang, Wei Ming Lin, Shu Wen Chen, Hsiang Wei Hu |
| المصدر: | Digital Health, Vol 10 (2024) |
| بيانات النشر: | SAGE Publishing, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics |
| مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7 |
| الوصف: | Background Vaginal birth after cesarean (VBAC) is generally regarded as a safe and viable birthing option for most women with prior cesarean delivery. Nonetheless, concerns about heightened risks of adverse maternal and perinatal outcomes have often dissuaded women from considering VBAC. This study aimed to assess the performance of an artificial intelligence (AI)-powered VBAC prediction system integrated into a decision-aid birth choice platform for shared decision-making (SDM). Materials and Methods Employing a retrospective design, we collected medical records from a regional hospital in northern Taiwan from January 2019 to May 2023. To explore a suitable model for tabular data, we compared two prevailing modeling approaches: tree-based models and logistic regression models. We subjected the tree-based algorithm, CatBoost, to binary classification. Results Forty pregnant women with 347 records were included. The CatBoost model demonstrated a robust performance, boasting an accuracy rate of 0.91 (95% confidence interval (CI): 0.86–0.94) and an area under the curve of 0.89 (95% CI: 0.86–0.93), surpassing both regression models and other boosting techniques. CatBoost captured the data characteristics on the significant impact of gravidity and the positive influence of previous vaginal birth, reinforcing established clinical guidelines, as substantiated by the SHapley Additive exPlanations analysis. Conclusion Using AI techniques offers a more accurate assessment of VBAC risks, boosting women’s confidence in selecting VBAC as a viable birthing option. The seamless integration of AI prediction systems with SDM platforms holds a promising potential for enhancing the effectiveness of clinical applications in the domain of women's healthcare. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2055-2076 20552076 |
| Relation: | https://doaj.org/toc/2055-2076 |
| DOI: | 10.1177/20552076241257014 |
| URL الوصول: | https://doaj.org/article/a6d91cd62981411981e1adb2e6311711 |
| رقم الأكسشن: | edsdoj.6d91cd62981411981e1adb2e6311711 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20552076 |
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| DOI: | 10.1177/20552076241257014 |