Early detection of intrapartum risks enables timely interventions to prevent or mitigate adverse labor outcomes such as cerebral palsy. However, accurate automated systems to support clinical decision-making during delivery are currently lacking. To address this gap, we propose Artificial Intelligence for Modeling and Explaining Neonatal Health (AIMEN), a deep learning framework that predicts adverse labor outcomes from maternal, fetal, obstetrical, and intrapartum factors while providing interpretable reasoning behind its predictions. AIMEN reveals how specific modifications to input variables could alter predicted outcomes, enhancing clinical insight. To address class imbalance and limited sample size, AIMEN employs Conditional Tabular GAN (CTGAN) for data augmentation. This process includes synthetic data generation, and we investigate in detail properties such as relaxing feature bounds for a subset of training points to explore slightly out-of-range physiological values, and applying silhouette-score-based filtering to increase the separability of synthetic samples. AIMEN uses an ensemble of fully connected neural networks for classification and outperforms state-of-the-art models such as XGBoost, TabNet, DANet, and LightGBM, achieving an average F1 score of 0.784 in predicting high-risk deliveries. Moreover, AIMEN generates counterfactual explanations that identify actionable changes involving only two to three attributes on average. Resources: https://github.com/ab9mamun/AIMEN.

翻译：产时风险的早期检测能够及时干预，从而预防或减轻脑瘫等不良分娩结局。然而，目前缺乏在分娩过程中辅助临床决策的准确自动化系统。为解决这一问题，我们提出了新生儿健康建模与解释人工智能（AIMEN）框架，这是一个基于深度学习的框架，能够根据母体、胎儿、产科及产时因素预测不良分娩结局，并为其预测提供可解释的推理。AIMEN揭示了输入变量的特定修改如何改变预测结果，从而增强临床洞察力。针对类别不平衡和样本量有限的问题，AIMEN采用条件表格生成对抗网络（CTGAN）进行数据增强。该过程包括合成数据生成，我们详细研究了其特性，例如放宽部分训练点的特征边界以探索略超范围的生理值，以及应用基于轮廓系数的过滤来提高合成样本的可分离性。AIMEN使用全连接神经网络集成进行分类，在预测高风险分娩方面优于XGBoost、TabNet、DANet和LightGBM等先进模型，平均F1分数达到0.784。此外，AIMEN生成的反事实解释能够识别仅涉及平均两到三个属性的可操作变化。资源：https://github.com/ab9mamun/AIMEN。