论文标题
使用可解释的机器学习来预测母亲和胎儿的结果
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
论文作者
论文摘要
大多数怀孕和出生会带来良好的结果，但是并不常见，当它们发生时，它们可能与对母亲和婴儿的严重影响有关。预测建模有可能通过更好地理解风险因素，增强监视以及更及时，更适当的干预措施来改善结果，从而帮助产科医生提供更好的护理。对于三种类型的并发症，我们使用可解释的增强机（EBM）（玻璃盒模型）来识别和研究最重要的风险因素，以获得清晰度：（i）严重的孕产妇发病率（SMM），（ii）（ii）肩部肌张力障碍和（iii）早产preclampsia。在利用EBM的解释性来揭示出对风险促成的特征的令人惊讶的见解时，我们的实验表明EBM与其他黑盒ML方法（例如深神经网和随机森林）的准确性相匹配。
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.
