Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes

Tomas Bosschieter, Zifei Xu, Hui Lan, Ben Lengerich, Harsha Nori, Kristin Sitcov, Vivienne Souter, Rich Caruana

Abstract:

Most pregnancies 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 understand-
ing of risk factors, heightened surveillance for high risk patients,
and more timely and appropriate interventions, thereby helping ob-
stetricians 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 intelli-
gibility: (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.