Interpretable Predictive Models to Understand Risk Factors for Maternal
  and Fetal Outcomes
Tomas M. Bosschieter Zifei Xu Hui Lan Benjamin J. Lengerich Harsha Nori Ian Painter Vivienne Souter Rich Caruana
Although most pregnancies result in a good outcome, complications are not
uncommon and 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 for high risk
patients, and more timely and appropriate interventions, thereby helping
obstetricians deliver better care. We identify and study the most important
risk factors for four types of pregnancy complications: (i) severe maternal
morbidity, (ii) shoulder dystocia, (iii) preterm preeclampsia, and (iv)
antepartum stillbirth. We use an Explainable Boosting Machine (EBM), a
high-accuracy glass-box learning method, for prediction and identification of
important risk factors. We undertake external validation and perform an
extensive robustness analysis of the EBM models. EBMs match the accuracy of
other black-box ML methods such as deep neural networks and random forests, and
outperform logistic regression, while being more interpretable. EBMs prove to
be robust. The interpretability of the EBM models reveals surprising insights
into the features contributing to risk (e.g. maternal height is the second most
important feature for shoulder dystocia) and may have potential for clinical
application in the prediction and prevention of serious complications in
pregnancy.