Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
This work addresses improving pregnancy outcomes for mothers and babies by identifying key risk factors, but it is incremental as it applies an existing interpretable method to a specific medical domain.
The study tackled predicting maternal and fetal complications like Severe Maternal Morbidity, shoulder dystocia, and preterm preeclampsia using Explainable Boosting Machine (EBM), achieving accuracy comparable to black-box methods such as deep neural nets and random forests.
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.