A team of researchers from Hunter College and Columbia University were named one of the institutional winners of the National Institutes of Health’s Decoding Maternal Morbidity Challenge. The goal of the challenge was to come up with new ways of analyzing data to identify factors that impact maternal morbidity in order to help clinicians identify and treat pregnancy-related conditions.
The team was awarded a $50,000 prize for innovation.
The Hunter participants included Computer Science Professor Anita Raja and students Adam Catto (MS ‘22), Daniel Mallia (MA ‘22), and Alisa Leshchenko ’23, who work in Professor Raja’s Distributed Artificial Intelligence Research (DAIR) Lab. Their teammates were four Colombia University computer science researchers—including Professor Ansaf Salleb-Aouissi—and two Columbia University Medical Center researchers.
The Hunter and Columbia team’s submission, “On Predicting and Understanding Preeclampsia: A Machine Learning Approach,” presented a new methodology and analysis for identifying patients with a high risk of preeclampsia early in pregnancy. Preeclampsia is considered a leading cause for maternal morbidity in the United States. Its quick development and unknown causes make it uniquely challenging to combat and there is currently no well-defined process on how to screen for and diagnose the condition. To address this, the team sought to create a composite predictive model of severe preeclampsia and eclampsia to aid in the diagnosis. The Hunter researchers’ work on early prediction and optimizing information about the scheduling of patient testing and doctor’s visits contributed substantially to the team’s approach.
This project builds off of the work that was seeded by Professor Raja’s NIH Grant to study the “Prediction of Preterm Birth in Nulliparous Women.” With over 26 billion dollars spent annually on the delivery and care of the 12% of infants who are born preterm in the United States, the research being done in Professor Raja’s DAIR Lab has the potential to make deep inroads into this long-lasting public health problem.
