Stanford University study shows prematurity complications predictions with medical data – India Education

By sifting through electronic health records of moms and babies using a machine-learning algorithm, scientists can predict how at-risk newborns will fare in their first two months of life. The new method allows physicians to classify, at or before birth, which infants are likely to develop complications of prematurity. A study describing the method, developed at the Stanford School of Medicine, was published online Feb. 15 in Science Translational Medicine. "This is a new way of thinking about preterm birth, placing the focus on individual health factors of the newborns rather than looking only at how early they are born," said senior study author Nima Aghaeepour, PhD, an associate professor of anesthesiology, perioperative and pain medicine and of pediatrics. The study's lead authors are postdoctoral scholar Davide De Francesco, PhD, and Jonathan Reiss, MD, an instructor in pediatrics.