Community Insights: Overcoming Medical Class Imbalance with Synthetic Data

In this case study, Reetam Ganguli, a medical candidate at Brown University and leader of a bioincubator, explains why medical practitioners turn to synthetic data when overcoming challenges with clinical data. Biased data or class imbalance is a significant problem in the medical field due to limited medical data collection from underrepresented demographics, historically low mortality rates for commonly treated diseases, and gender biases stemming from societal and clinical factors. Reetam's team leverages synthetic data to predict postpartum hemorrhages for expecting mothers in Cameroon and Nigeria. Synthetic data can help combat this critical data challenge by generating diverse datasets that enable better research outcomes.