Identifying risk of stillbirth using machine learning.

Applying advanced machine learning techniques to a comprehensive database of stillbirths and live births with unique and clinically relevant variables resulted in an algorithm that could accurately identify 85% of pregnancies that would result in stillbirth, before they reached viability. Once validated in representative databases reflective of the United States birthing population and then prospectively, these models may provide effective risk stratification and clinical decision-making support to better identify and monitor those at risk for stillbirth.