Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through better understanding of risk factors, heightened surveillance, and more timely and appropriate interventions, thereby helping obstetricians deliver better care. For three types of complications we identify and study the most important risk factors using Explainable Boosting Machine (EBM), a glass box model, in order to gain intelligibility: (i) Severe Maternal Morbidity (SMM), (ii) shoulder dystocia, and (iii) preterm preeclampsia. While using the interpretability of EBM's to reveal surprising insights into the features contributing to risk, our experiments show EBMs match the accuracy of other black-box ML methods such as deep neural nets and random forests.

翻译：大多数怀孕和生育都产生了良好的结果,但并发症并不罕见,而且一旦发生,它们就会对母亲和婴儿产生严重影响。预测模型通过更好地了解风险因素、加强监督以及更及时和适当的干预措施,从而帮助产科医生提供更好的护理,有可能改善结果。对于三种类型的并发症,我们利用一个玻璃盒模型——解释性诱导机(EBM)确定和研究最重要的风险因素,以便获得感知性:(一) 严重产妇疾病(SMM)、(二) 肩部抑郁症(SMM)和(三) 预产期前。在利用EBM的可解释性来揭示出令人惊讶的关于造成风险的特点的洞见的同时,我们的实验显示EBMS与深神经网和随机森林等其他黑盒ML方法的准确性相匹配。