Using machine learning to sift through the electronic health records of both mothers and newborns can predict how premature babies will fare in their first two months of life, researchers report.

The new method, reported in the journal Science Translational Medicine, allows physicians to classify, at or before birth, which infants are likely to develop complications of prematurity.

“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,” says senior author Nima Aghaeepour, an associate professor of anesthesiology, perioperative and pain medicine and of pediatrics Stanford University School of Medicine.

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.

“Preterm birth is the single largest cause of death in children under age 5 worldwide, and we haven’t had good solutions,” Aghaeepour says. “By focusing our research on predicting the health of these babies, we can optimize their care.”

Many complications of prematurity take days or weeks after birth to emerge, causing substantial damage to newborns’ health in the meantime. Knowing which infants are at risk could enable preventive measures.

“We look mainly at the baby to make treatment decisions in neonatology, but we are finding that we can get valuable information from the maternal health record, really homing in on how individual babies’ trajectories have been shaped by exposure to their specific maternal environment,” says coauthor David Stevenson, a neonatologist at Lucile Packard Children’s Hospital Stanford, professor of pediatrics, and director of the March of Dimes Prematurity Research Center at the Stanford School of Medicine.

“This is a move toward precision medicine for babies,” he adds.

Read the full article about using AI to predict babies' health risk by Erin Digitale at Futurity.