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Data-Driven Risk Stratification for Preterm Birth in Brazil: Development of a Machine Learning-Based Innovation for Health Care

Identifying the preventable causes and performing early risk stratification of pregnant women are instrumental to develop strategies to prevent and reduce preterm birth (PTB). The ability to identify at-risk pregnancies and to enroll women in prevention strategies has been difficult due to complexity of associated risk factors. The study aims to combine different national level data sources to understand the main predictors of PTB and develop a machine-learning-based predictive model to conduct automated risk stratification at the point of care level, integrated with advanced data visualization for clinical decision support.

More information about Grand Challenges Explorations - Brazil: Data Science Approaches to Improve Maternal and Child Health in Brazil

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The Bill & Melinda Gates Foundation is part of the Grand Challenges partnership network. Visit grandchallenges.org to view the map of awarded grants across this network and grant opportunities from partners.