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Evaluating the Performance of an AI-powered VBAC Prediction System Within a Decision-Aid Birth Choice Platform for Shared Decision-Making

Digital health(2024)SCI 4区

St Pauls Hosp

Cited 1|Views8
Abstract
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.
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Key words
Vaginal birth after cesarean,elective repeat cesarean delivery,shared decision making,artificial intelligence prediction,pregnant women
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要点】:本研究评估了集成在决策辅助分娩选择平台中的人工智能 VBAC 预测系统的性能,结果显示该系统提高了对 VBAC 风险的准确评估,增强了女性选择 VBAC 作为分娩方式信心。

方法】:研究采用回顾性设计,比较了基于树的模型(CatBoost)和逻辑回归模型,通过医疗记录数据训练并评估模型性能。

实验】:收集了台湾北部一家地区医院从 2019 年 1 月至 2023 年 5 月的 40 名孕妇的 347 条记录,使用 CatBoost 模型进行二分类,结果显示模型准确率为 0.91,AUC 为 0.89,优于其他模型和提升方法。