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, Ph.D., an associate professor of anesthesiology, perioperative and pain medicine and of pediatrics. The study's lead authors are postdoctoral scholar Davide De Francesco, Ph.D., and Jonathan Reiss, MD, an instructor in pediatrics.

Traditionally defined as birth occurring at least three weeks early, premature birth is linked to complications in babies' lungs, brains, vision, hearing and digestive system. Although earlier births generally carry higher risks, the timing of birth predicts only approximately how a specific infant will fare. Some infants who are born quite early develop no complications, while others born at the same stage of pregnancy become very ill or die.